DataPro

BixBench to Evaluate AI Agents on Real-World Bioinformatics Task❯❯❯❯ Python Machine Learning By Example: Written by Yuxi (Hayden) Liu, Python Machine Learning by Example, Fourth Edition is a hands-on guide covering NLP transformers, PyTorch, computer vision, and deep learning. It emphasizes best practices for building and improving real-world machine learning models using Python.Buy eBook $36.99 $24.99📢 Welcome to DataPro #129 ~ Your Weekly Dose of Data Science & ML Innovation!The world of AI is evolving at lightning speed, and we’re here to keep you ahead of the curve! This week’s edition is packed with cutting-edge AI model evaluations, innovative MLOps tools, and groundbreaking advancements in agentic AI and retrieval-augmented generation (RAG).𖣠What’s Inside?🔍 Model Analysis & AI Performance – Explore how Vertex AI, LLM Comparator, and BentoML streamline AI evaluation and deployment.🧠 Advanced Reasoning Models – Dive into DeepSeek-R1’s reinforcement learning breakthroughs and OpenAI’s o1 model’s test-time compute scaling.🧪️Practical AI Use Cases – Learn how Unico is revolutionizing IDTech with Spanner Vector Search and how Agentic Knowledge Distillation enhances RAG efficiency.🎲MLOps & Data Science Essentials – Discover Python one-liners for Scikit-Learn, Streamlit for real-time crypto analysis, and the Defog AI’s Introspect.🤖 AI Alignment & Ethics – Tackle the growing concerns of deep scheming in agentic AI and why Intrinsic AI Alignment (IAIA) is critical for the future of responsible AI.Stay informed, stay innovative, and let’s dive into the latestdata and AIbreakthroughs together! 🚀Cheers,Merlyn ShelleyGrowth Lead, Packt❯❯❯❯ Microsoft Power BI Cookbook: Written by Greg Deckler and Brett Powell, Microsoft Power BI Cookbook (3rd Edition) is a detailed guide for data professionals, covering data integration, Hybrid tables, scorecards, real-time processing, governance, security, and advanced visualization. With step-by-step techniques, it helps you transform raw data into actionable insights using Power BI’s latest innovations.Buy eBook $43.99 $29.99🔍 Fresh Insights ⋆✴︎˚｡⋆𖤐 Evaluate AI models with Vertex AI & LLM Comparator: This blog explores how to evaluate generative AI models using Vertex AI evaluation service and LLM Comparator. It explains pairwise model evaluation, a method to compare two models directly for better decision-making. The Vertex AI evaluation service helps with model selection, optimization, fine-tuning, and benchmarking, while the LLM Comparator offers an intuitive, human-in-the-loop approach for side-by-side comparisons. The post highlights how to define custom metrics, leverage automated and manual assessments, and streamline workflows with integrated tracking. Plus, new users can access $300 in free credit to test Google Cloud AI/ML services.𖤐 Time series forecasting with LLM-based foundation models and scalable AIOps on AWS: This blog explores how Chronos, an LLM-based foundation model, enhances time series forecasting with Amazon SageMaker Pipelines. Traditional forecasting requires extensive tuning, but Chronos leverages LLM architectures to generalize across domains and perform zero-shot predictions. The post covers integrating Chronos into SageMaker, generating synthetic data, fine-tuning, and optimizing models with hyperparameter search. Key highlights include reduced processing time, automated workflows, and scalable AIOps on AWS for improved forecasting efficiency. Readers will gain hands-on knowledge to streamline model deployment and enhance forecasting capabilities.𖤐 Manhattan Associates Discovers the Power of Deeply Connected Data Pipelines: Manhattan Associates streamlined data pipeline automation using CData Sync, overcoming connectivity issues and unpredictable costs. Key benefits include instant replication of 200+ Jira fields, agility in SQL Server data movement, and 50% cost savings with fixed pricing. CData Sync’s deep API connections enable scalable, error-free data integration across cloud and on-premises environments, eliminating the need for intensive monitoring. With efficient, connected pipelines, Manhattan Associates improved productivity, ensuring accurate, timely data for supply chain operations.𖤐 BentoML: MLOps for Beginners. This blog introduces BentoML, a beginner-friendly MLOps framework that simplifies model deployment with minimal DevOps expertise. It covers building a Text-to-Speech app, creating Docker images, and deploying models to BentoCloud using simple CLI commands. Readers learn how BentoML automates infrastructure, integrates with transformers, and scales AI services efficiently. The guide includes a hands-on tutorial for setting up, deploying, and monitoring machine learning models with GPU support for optimized inference.𖤐 10 Python One-Liners for Scikit-learn. This blog highlights 10 essential Python one-liners for Scikit-Learn, streamlining machine learning workflows. It covers data preprocessing, model training, evaluation, and automation with concise, efficient code. Learn how to import modules, split datasets, standardize features, train SVM models, perform PCA, generate reports, and build pipelines, all in just one line each. Ideal for quick experiments, prototyping, and simplifying repetitive tasks, these snippets help you write cleaner, more efficient code while improving model performance and workflow clarity.𖤐 Using GPT-4.5 Without a $200 Subscription: This blog reveals how to access GPT-4.5 without a $200 subscription using the OpenAI API Playground for as little as $0.10–$0.30 per request. It guides users through creating an OpenAI account, adding credits, selecting GPT-4.5-preview, and integrating the API into applications. While cost-effective, it remains one of OpenAI’s most expensive models, so users should consider it for high-value tasks. The article highlights GPT-4.5’s accuracy, human-like responses, and seamless API integration, making advanced AI more affordable for developers and AI enthusiasts.❯❯❯❯ Deep Reinforcement Learning Hands-On: Written by Maxim Lapan, Deep Reinforcement Learning Hands-On (3rd Edition) is a detailed guide to mastering RL, covering Q-learning, DQNs, PPO, RLHF, MuZero, and transformers. With hands-on projects, it helps machine learning professionals build, train, and apply RL models using PyTorch for real-world tasks in gaming, finance, and beyond.Buy eBook $46.99 $31.99🚀 Trendspotting: What's Next in Tech Trends𖤐 Beyond Monte Carlo Tree Search: Unleashing Implicit Chess Strategies with Discrete Diffusion. This blog explores DIFFUSEARCH, a discrete diffusion-based framework that enhances long-term planning in large language models (LLMs) without costly search algorithms like MCTS. Unlike traditional methods prone to error propagation, DIFFUSEARCH iteratively refines future predictions using diffusion models, improving decision accuracy and efficiency. Evaluated on chess games, it outperformed state-action models by 653 Elo, achieving higher accuracy with fewer data. Beyond chess, this implicit search method offers potential applications in AI planning, structured writing, and next-token prediction, marking a step forward in long-term reasoning for LLMs.𖤐 Forrester TEI study on Spanner shows benefits and cost savings: This blog explores the economic impact of Google Cloud’s Spanner, based on a Forrester TEI study, showing a 132% ROI over three years. Organizations benefit from $7.74M in cost savings, including $3.8M from retiring legacy databases, $1.2M from eliminating downtime, and $1M from reduced overprovisioning. Spanner’s scalability, reliability (99.999% uptime), and automation enable faster onboarding, improved budget predictability, and enhanced innovation. Beyond cost savings, it streamlines operations, reduces engineering workload, and supports agile development, making it a powerful alternative to legacy database systems.𖤐 Advancing biomedical discovery: Overcoming data challenges in precision medicine. This blog explores a Microsoft Research study on biomedical data challenges, highlighting data procurement issues, computational hurdles, and collaboration bottlenecks in precision medicine. Key recommendations include standardizing workflows, improving secure data-sharing, and leveraging AI for automation. A unified biomedical data lifecycle can enhance interoperability, reproducibility, and research efficiency. The study emphasizes cloud-based infrastructures to democratize data access and accelerate scientific discovery. By breaking data silos, researchers can advance individualized therapeutics, paving the way for more robust biomedical research and clinical innovation.𖤐 Researchers from FutureHouse and ScienceMachine Introduce BixBench: A Benchmark Designed to Evaluate AI Agents on Real-World Bioinformatics Task. BixBench evaluates AI performance in bioinformatics through 53 real-world analytical tasks, emphasizing multi-step reasoning. AI models like GPT-4o achieved only 17% accuracy, revealing challenges in scientific data analysis. This benchmark guides AI advancements in bioinformatics research.𖤐 Defog AI Open Sources Introspect: MIT-Licensed Deep-Research for Your Internal Data. Defog AI’s Introspect is an open-source AI tool that unifies structured and unstructured data research across SQL, PDFs, and web search. Using a Sonnet agent with recursive tool calling, it automates deep research, improving efficiency and insight extraction. Supporting major databases like PostgreSQL, Snowflake, and BigQuery, Introspect simplifies internal data analysis, reducing silos and manual effort. With an MIT license and active community, it’s a powerful solution for enterprises and developers looking to enhance AI-driven research and decision-making.𖤐 Unico builds cutting-edge IDTech with Spanner Vector Search: Unico, a leading biometric verification company, uses Google Cloud Spanner to power vector search for facial authentication. Handling 1.2 billion authentications, Unico prevents $14 billion in fraud and processes 35 million new faces monthly. Spanner’s vector search, with low latency, high accuracy (96%), and scalability, enables real-time fraud detection and secure identity verification. With Google Cloud’s support, Unico aims for global expansion, advancing AI-driven identity solutions beyond Brazil.𖤐 A Step by Step Guide to Deploy Streamlit App Using Cloudflared, BeautifulSoup, Pandas, Plotly for Real-Time Cryptocurrency Web Scraping and Visualization. This tutorial guides you through building and deploying a real-time cryptocurrency dashboard using Streamlit, BeautifulSoup, Pandas, and Plotly. It scrapes live crypto prices from CoinMarketCap, visualizes them with interactive charts, and deploys via Cloudflared for seamless public access. With bar and pie charts for price and market cap analysis, the app updates dynamically. Using Google Colab and Cloudflared, this approach ensures easy, authentication-free deployment, making it ideal for beginners and developers looking to create and share interactive data-driven web apps effortlessly.❯❯❯❯ Data Management Strategy at Microsoft: Written by Aleksejs Plotnikovs, Data Management Strategy at Microsoft is a practical guide to building a data-driven culture and maximizing data’s business value. Covering data strategy, governance, change management, and intellectual property, it provides key insights from Microsoft’s decade-long transformation to help leaders drive impactful data initiatives.Buy eBook $31.99 $21.99🛠️ Platform Showdown: Comparing ML Tools & Services𖤐 Mastering 1:1s as a Data Scientist: From Status Updates to Career Growth: This blog explores effective 1:1 meetings for data scientists and analysts, covering regular scheduling, structured agendas, and key discussion topics. It emphasizes tracking achievements, resolving blockers, career growth discussions, and feedback exchanges. A well-prepared 1:1 document enhances communication, accountability, and performance reviews. Managers should align priorities, offer guidance, and foster career development. By integrating project updates, feedback loops, and company goals, these meetings strengthen relationships, boost productivity, and support long-term career progression in data teams.𖤐 Magma: A foundation model for multimodal AI agents across digital and physical worlds. Magma is a multimodal AI foundation model that integrates visual perception, language comprehension, and action reasoning across digital and physical environments. Unlike traditional VLA models, Magma enables AI agents and robots to generalize tasks efficiently, from UI navigation to real-world interactions. It introduces Set-of-Mark (SoM) and Trace-of-Mark (ToM) for structured task understanding and outperforms state-of-the-art models in zero-shot and finetuning evaluations. Available on Azure AI Foundry Labs and Hugging Face, Magma represents a step toward advanced AI-driven automation and decision-making.𖤐 Meet AI Co-Scientist: A Multi-Agent System Powered by Gemini 2.0 for Accelerating Scientific Discovery. The AI co-scientist, developed by Google Cloud AI, DeepMind, and Stanford, is a multi-agent system designed to accelerate biomedical discovery. It employs a "generate, debate, and evolve" framework using test-time compute scaling for improved hypothesis generation in drug repurposing, target discovery, and bacterial evolution. With specialized agents for ranking, clustering, and refining hypotheses, it achieves 78.4% top-1 accuracy and outperforms baseline models in novelty and impact. This AI-driven approach bridges disciplines, transforming scientific research collaboration and discovery.𖤐 DeepSeek AI Releases Smallpond: A Lightweight Data Processing Framework Built on DuckDB and 3FS. Smallpond, developed by DeepSeek AI, extends DuckDB into a distributed data processing framework using 3FS. It enables high-performance SQL analytics across large datasets without complex infrastructure. Supporting Python 3.8–3.12, Smallpond integrates Ray for parallel processing, offering scalability and flexibility. Benchmarked at 3.66TiB/min, it efficiently processes terabyte-scale data. With a lightweight, modular design, Smallpond simplifies distributed workflows, reducing maintenance overhead while maintaining high-throughput performance. As an open-source project, it fosters collaboration and innovation for modern data engineering.𖤐 IBM AI Releases Granite 3.2 8B Instruct and Granite 3.2 2B Instruct Models: Offering Experimental Chain-of-Thought Reasoning Capabilities. IBM Research AI introduces Granite 3.2, a family of instruction-tuned LLMs optimized for enterprise applications. The Granite 3.2-2B model prioritizes low-latency inference, while the 8B model delivers higher accuracy in structured tasks. Leveraging self-distillation and custom instruction tuning, these models achieve 82.6% accuracy in domain-specific retrieval and 97% reliability in multi-turn conversations. The 2B variant reduces latency by 35%, making it ideal for fast-response AI solutions. Released under Apache 2.0, Granite 3.2 provides a scalable, efficient alternative for business-ready AI deployment.𖤐 HippoRAG 2: Advancing Long-Term Memory and Contextual Retrieval in Large Language Models. HippoRAG 2, developed by Ohio State University and UIUC, enhances retrieval-augmented generation (RAG) by integrating structured knowledge graphs for improved factual recall and multi-hop reasoning. Using Personalized PageRank (PPR) and recognition memory, it boosts retrieval accuracy by 7% over leading models. Evaluated against BM25, GraphRAG, and LightRAG, it excels in QA, associative memory, and discourse understanding. By linking contextual information, HippoRAG 2 advances LLM continual learning, offering a neurobiology-inspired long-term memory framework that refines AI sense-making and reasoning capabilities.❯❯❯❯ Polars Cookbook: Written by Yuki Kakegawa, Polars Cookbook is a hands-on guide featuring 60+ real-world projects to master data manipulation, transformation, and analysis with Python Polars. Covering advanced querying, performance optimization, and integrations with pandas, PyArrow, and cloud platforms, this book helps data professionals build fast, scalable, and efficient workflows.Buy eBook $46.99 $31.99📊 Success Stories: Real-World ML Case Studies𖤐 LLM + RAG: Creating an AI-Powered File Reader Assistant. This blog explores Retrieval-Augmented Generation (RAG), a technique that enhances LLMs by integrating external knowledge bases for more accurate, domain-specific responses. Unlike retraining large models, RAG dynamically retrieves relevant data at inference, reducing hallucinations and improving contextual accuracy. The article details a Streamlit-based AI-powered PDF reader, leveraging LangChain, OpenAI’s GPT-4, and FAISS for efficient document retrieval and Q&A. By embedding and vectorizing text, RAG enables structured information retrieval, making AI smarter and more adaptable for enterprise applications.𖤐 One-Tailed Vs. Two-Tailed Tests: This blog explores the differences between one-tailed and two-tailed hypothesis tests in A/B testing, explaining their impact on sample size, statistical power, and result interpretation. A one-tailed test detects a specific direction of change, requiring a smaller sample size, while a two-tailed test accounts for both positive and negative effects, offering greater flexibility but requiring more data. The choice depends on business objectives, with one-tailed tests favoring metric improvements and two-tailed tests ensuring unbiased evaluation. Understanding these trade-offs helps optimize testing strategies and resource allocation in data-driven decision-making.𖤐 Generative AI Is Declarative: This article explores how generative AI operates in a declarative mode, focusing on what users want rather than how to achieve it. Like ordering a cheeseburger, interactions with LLMs involve iterative refinement, as missing details are inferred rather than explicitly requested. Declarative AI interaction simplifies user experience but requires clear prompting strategies and evaluation mechanisms to ensure quality responses. Understanding general vs. non-general information helps optimize AI applications, balancing fresh data retrieval, privacy concerns, and structured prompts for better human-AI collaboration in real-world tasks.𖤐 Overcome Failing Document Ingestion & RAG Strategies with Agentic Knowledge Distillation: This blog explores Agentic Knowledge Distillation + Pyramid Search, a novel approach to improving Retrieval-Augmented Generation (RAG). By distilling critical information at ingestion, this method enhances retrieval efficiency, response accuracy, and scalability for complex, multi-document research tasks. It outperforms traditional RAG by reducing cognitive load, preserving context, and optimizing token usage, making AI-driven analysis more reliable and insightful.𖤐 The Urgent Need for Intrinsic Alignment Technologies for Responsible Agentic AI: This blog examines the emerging risks of deep scheming in AI, where autonomous AI agents manipulate actions and communications to achieve goals. It introduces Intrinsic AI Alignment (IAIA), a novel approach ensuring AI’s internal reasoning aligns with ethical principles, beyond external guardrails.𖤐 How to Train LLMs to “Think” (o1 & DeepSeek-R1)? This blog explores how DeepSeek-R1 replicated OpenAI’s o1 model’s advanced reasoning, detailing the use of reinforcement learning (RL), thinking tokens, and test-time compute scaling to improve LLMs’ problem-solving and decision-making capabilities.❯❯❯❯Modern Time Series Forecasting with Python: Written by Manu Joseph and Jeffrey Tackes, Modern Time Series Forecasting with Python (2nd Edition) is a detailed guide for data professionals, covering machine learning, deep learning, transformers, probabilistic forecasting, feature engineering, and ensemble methods. With hands-on techniques, it helps you build, evaluate, and deploy advanced forecasting models using Python, PyTorch, and pandas.Buy eBook $46.99 $31.99❯❯❯❯ Python Feature Engineering Cookbook: Written by Galli, Python Feature Engineering Cookbook (3rd Edition) is a practical guide featuring real-world techniques to craft powerful features for tabular, transactional, and time-series data. Covering imputation, encoding, transformation, feature extraction, and automation, this book helps data professionals build efficient, reproducible, and production-ready feature engineering pipelines.Buy eBook $35.99 $24.99We’ve got more great things coming your way, see you soon!*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}

Data Management Strategy at Microsoft, Building Multimodal Search Agents with BLIP-2 and Gemini👋 Hello ,📢 Welcome toDataPro #128~ Your Weekly Dose of Data Science & ML Innovation!The world of AI, machine learning, and data science never slows down, and neither do we! This week’s edition is packed with breakthroughs, must-know tools, and career insights to keep you ahead of the curve.🔹 Data & ML Reads: Explore Python Machine Learning By Example, Power BI mastery, deep reinforcement learning, and high-performance data manipulation with Polars.🔍 Fresh Insights: A 27-day AI coding experiment, deep dive into LLMs, and why data scientists should embrace Docker.🚀 Tech Trends: Advanced Time Intelligence in DAX, Multimodal search with BLIP-2 & Gemini, and Sparse Autoencoders in LLMs.🛠️ ML Tool Showdown: Discover MoBA’s new attention mechanism, Microsoft’s Magma AI for robotics & UI, and Mistral Saba’s breakthrough in Arabic & Tamil NLP.📊 Success Stories: Free interactive data visualizations with Marimo, SQLite-powered RAG, and how Decision Intelligence is shaping the future of data.💡 Your AI & ML Knowledge Hub is Here! Dive into these game-changing trends, tools, and innovations.🔗 Read it all now! ⬇️Cheers,Merlyn ShelleyGrowth Lead, Packt📚 Packt Signature Series: New Releases You Can't Miss❯❯❯❯ Python Machine Learning By Example: Written by Yuxi (Hayden) Liu, Python Machine Learning by Example, Fourth Edition is a hands-on guide covering NLP transformers, PyTorch, computer vision, and deep learning. It emphasizes best practices for building and improving real-world machine learning models using Python.Buy eBook $36.99 $24.99❯❯❯❯ Microsoft Power BI Cookbook: Written by Greg Deckler and Brett Powell, Microsoft Power BI Cookbook (3rd Edition) is a detailed guide for data professionals, covering data integration, Hybrid tables, scorecards, real-time processing, governance, security, and advanced visualization. With step-by-step techniques, it helps you transform raw data into actionable insights using Power BI’s latest innovations.Buy eBook $43.99 $29.99❯❯❯❯Modern Time Series Forecasting with Python: Written by Manu Joseph and Jeffrey Tackes, Modern Time Series Forecasting with Python (2nd Edition) is a detailed guide for data professionals, covering machine learning, deep learning, transformers, probabilistic forecasting, feature engineering, and ensemble methods. With hands-on techniques, it helps you build, evaluate, and deploy advanced forecasting models using Python, PyTorch, and pandas.Buy eBook $46.99 $31.99❯❯❯❯ Deep Reinforcement Learning Hands-On: Written by Maxim Lapan, Deep Reinforcement Learning Hands-On (3rd Edition) is a detailed guide to mastering RL, covering Q-learning, DQNs, PPO, RLHF, MuZero, and transformers. With hands-on projects, it helps machine learning professionals build, train, and apply RL models using PyTorch for real-world tasks in gaming, finance, and beyond.Buy eBook $46.99 $31.99❯❯❯❯ Polars Cookbook: Written by Yuki Kakegawa, Polars Cookbook is a hands-on guide featuring 60+ real-world projects to master data manipulation, transformation, and analysis with Python Polars. Covering advanced querying, performance optimization, and integrations with pandas, PyArrow, and cloud platforms, this book helps data professionals build fast, scalable, and efficient workflows.Buy eBook $46.99 $31.99❯❯❯❯ Python Feature Engineering Cookbook: Written by Galli, Python Feature Engineering Cookbook (3rd Edition) is a practical guide featuring real-world techniques to craft powerful features for tabular, transactional, and time-series data. Covering imputation, encoding, transformation, feature extraction, and automation, this book helps data professionals build efficient, reproducible, and production-ready feature engineering pipelines.Buy eBook $35.99 $24.99❯❯❯❯ Data Management Strategy at Microsoft: Written by Aleksejs Plotnikovs, Data Management Strategy at Microsoft is a practical guide to building a data-driven culture and maximizing data’s business value. Covering data strategy, governance, change management, and intellectual property, it provides key insights from Microsoft’s decade-long transformation to help leaders drive impactful data initiatives.Buy eBook $31.99 $21.99🔍 Fresh Insights ⋆✴︎˚｡⋆❯❯❯❯ Zero Human Code: What I Learned from Forcing AI to Build (and Fix) Its Own Code for 27 Straight Days: This blog explores a 27-day experiment where AI tools handled all coding, debugging, and implementation while the author acted solely as an orchestrator. It reveals the real limitations of AI-driven development, challenges in guiding AI, and key insights into prompting, system complexity, and architectural rigidity.❯❯❯❯ How LLMs Work: Pre-Training to Post-Training, Neural Networks, Hallucinations, and Inference: This blog provides a deep dive into how large language models (LLMs) work, covering their pre-training, post-training, neural network mechanics, inference, and hallucinations. It explains how LLMs are built, trained, fine-tuned, and optimized for real-world applications.❯❯❯❯ Why Data Scientists Should Care about Containers and Stand Out with This Knowledge: This blog explains why data scientists should understand containers, particularly Docker, to enhance model deployment, reproducibility, cloud integration, and scalability. It covers key concepts, practical applications, and provides a beginner-friendly guide to setting up a Jupyter Notebook in a Docker container.🚀 Trendspotting: What's Next in Tech Trends❯❯❯❯ Advanced Time Intelligence in DAX with Performance in Mind: This blog explores advanced time intelligence techniques in DAX, focusing on handling complex date-related calculations while optimizing performance. It covers scenarios like last N periods, leap years, week-to-date sums, and fiscal week YTD, using an extended date table for efficiency.❯❯❯❯ Multimodal Search Engine Agents Powered by BLIP-2 and Gemini: This blog explores how multimodal search engine agents powered by BLIP-2 and Gemini enhance e-commerce by enabling text and image-based searches. It explains BLIP-2’s architecture, training process, and loss functions, demonstrating its application in a fashion assistant for improved product discovery.❯❯❯❯ Formulation of Feature Circuits with Sparse Autoencoders in LLM: This blog explores how sparse autoencoders help disentangle feature circuits in large language models (LLMs), focusing on subject-verb agreement. It demonstrates how an LLM processes grammatical rules, visualizing feature circuits in both toy models and GPT-2 to enhance interpretability and debugging.🛠️ Platform Showdown: Comparing ML Tools & Services❯❯❯❯Moonshot AI Research Introduce Mixture of Block Attention (MoBA): A New AI Approach that Applies the Principles of Mixture of Experts (MoE) to the Attention Mechanism. This blog introduces Mixture of Block Attention (MoBA), a new AI approach that applies Mixture of Experts (MoE) principles to Transformer attention. MoBA improves efficiency in long-context processing by learning which token blocks to focus on, reducing computational costs while maintaining performance.❯❯❯❯ Microsoft Researchers Present Magma: A Multimodal AI Model Integrating Vision, Language, and Action for Advanced Robotics, UI Navigation, and Intelligent Decision-Making. This blog introduces Magma, a multimodal AI model by Microsoft Research that integrates vision, language, and action for robotics, UI navigation, and intelligent decision-making. Magma outperforms existing models by combining deep learning architectures, spatial reasoning, and large-scale pretraining for superior multimodal task execution.❯❯❯❯ Mistral AI Introduces Mistral Saba: A New Regional Language Model Designed to Excel in Arabic and South Indian-Origin Languages such as Tamil. This blog introduces Mistral Saba, a 24-billion-parameter AI model designed by Mistral AI to enhance Arabic and South Indian-origin languages like Tamil. With advanced NLP techniques and regional training, Mistral Saba delivers efficient, context-aware, and cost-effective AI solutions for diverse dialects and cultural nuances.📊 Success Stories: Real-World ML Case Studies❯❯❯❯Publish Interactive Data Visualizations for Free with Python and Marimo: This blog explores Marimo, a newly released Python library for publishing interactive data visualizations without the need for costly servers. Combining the ease of Jupyter notebooks with Pyodide/WASM, Marimo allows data scientists to create and share interactive web-based visualizations seamlessly and for free.❯❯❯❯ Roadmap to Becoming a Data Scientist, Part 4: Advanced Machine Learning. This blog explores advanced machine learning skills essential for data scientists, covering NLP, computer vision, reinforcement learning, and optimization techniques like fine-tuning and quantization. It emphasizes the evolution of ML methods, key concepts in LLMs, embeddings, and time series analysis, and strategies to stay competitive in the fast-changing AI landscape.❯❯❯❯ Retrieval Augmented Generation in SQLite: This blog explores Retrieval-Augmented Generation (RAG) with SQLite, showing how to perform vector search and generative AI integration using only SQLite, the sqlite-vec extension, and OpenAI embeddings, without relying on cloud vector databases. It provides a step-by-step guide to setting up a single-file RAG system, covering virtual tables, embeddings, and querying techniques for efficient, lightweight AI applications.❯❯❯❯ The Future of Data: How Decision Intelligence is Revolutionizing Data: This blog explores Decision Intelligence (DI), a rapidly growing field that combines AI, data science, and behavioral sciences to improve decision-making. It explains how DI differs from AI, its practical applications, and how organizations can leverage it for better predictions, automation, and efficiency across industries like retail, healthcare, finance, and manufacturing.We’ve got more great things coming your way, see you soon!*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}

Anthropic Economic Index, Microsoft’s ExACT, OpenThinker-32B, Huginn-3.5BYour Voice Matters – Help Improve Data Science Learning! (+ Get a Free eBook!)Help us enhance Data Science learning with your insights! Take a 5-8 min survey and get:✅ A free Packt eBook ($18 value)✅ Influence future books & courses✅ Early access to new features & perks!👉Take the Survey Now!📢 Welcome toDataPro #127~ Your Weekly Dose of Data Science & ML Innovation!The world of AI, machine learning, and data science never slows down, and we’re here to keep you on top of the latest innovations! From OpenAI’s o1 model for financial analysis to ArcticDB’s performance against Pandas, and Meta AI’s new CoCoMix framework, this edition is packed with breakthroughs, must-know tools, and career insights.🌟 Special Feature: An exclusive look at Python for Algorithmic Trading Cookbook by Jason Strimpel, helping you design, backtest, and deploy trading strategies with Python!🚀 In This Issue:🔹 Financial Analysis & AI: OpenAI’s o1 model is transforming financial research.🔹 Big Data Handling: Why ArcticDB is outperforming Pandas for massive datasets.🔹 AI Pretraining Advances: Meta AI introduces CoCoMix, improving LLM efficiency🔹 Scaling Vision-Language Models: Google DeepMind’s WebLI-100B dataset enhances cultural diversity.🔹 AI & Databases: Google’s Gen AI Toolbox for Databases streamlines AI-driven data access.💡 Also Inside:🔹 Microsoft’s ExACT framework for AI decision-making.🔹 Anthropic’s Economic Index: A deep dive into AI’s real-world economic role.🔹 OpenThinker-32B: A powerful open-data reasoning model.🔹 Huginn-3.5B: A new approach to scalable latent AI computation.🔹5 LLM Prompting Techniques that every developer should master.📩 Enjoy this issue & stay ahead in the Data & AI game!💬 Got a topic you’d love us to cover? Let us know!Cheers,Merlyn ShelleyGrowth Lead, Packt.✨ Featured Deep DiveOptimizing Financial Market Data with pandas: Analysis and TransformationHave you ever found yourself drowning in rows and columns of market data, wondering how to make sense of it all?You’re not alone.The good news is that pandas is here to help!Originally created by Wes McKinney at AQR Capital Management, pandas has grown into the go-to Python library for financial data analysis. Since its open-source release in 2009, it has empowered analysts to manipulate, transform, and analyze data with ease.If you work with financial data, whether for trading, risk management, or portfolio analysis, you need pandas in your toolkit. With its rich support for time series, data transformation, and handling missing values, pandas is the perfect solution for making sense of complex datasets. In this guide, we’ll walk through the essentials of pandas for financial market analysis. By the end, you’ll have the confidence to apply these tools in real-world scenarios.Getting Started with pandas Data StructuresBuilding Series and DataFramesThink of aSeriesas a column in Excel, but smarter. It’s a one-dimensional labeled array that can hold any data type. Let’s create a simple Series:import pandas as pd import numpy as np# Create a simple Series series = pd.Series([10, 20, 30, 40, 50], index=['A', 'B', 'C', 'D', 'E']) print(series)On the other hand, aDataFrameis a full table, imagine a spreadsheet where every column is a Series.# Creating a DataFrame data = {'Stock': ['AAPL', 'MSFT', 'GOOG'], 'Price': [150, 300, 2800], 'Volume': [10000, 15000, 12000]} df = pd.DataFrame(data) print(df)DataFrames make it easy to manipulate and analyze structured data, which is essential in financial market analysis.Handling Indexes in Financial DataUnderstanding pandas IndexingIndexes help you efficiently retrieve and align data. Different types include:Int64Index: Standard integer-based indexing.DatetimeIndex: Perfect for time series data.MultiIndex: Allows hierarchical indexing, great for financial datasets.Creating a DatetimeIndex for Financial Datadates = pd.date_range("2023–01–01", periods=10, freq='D') print(dates)MultiIndex for Market Datatuples = [('2023–07–10', 'AAPL'), ('2023–07–10', 'MSFT'), ('2023–07–10', 'GOOG')] multi_index = pd.MultiIndex.from_tuples(tuples, names=["date", "symbol"]) print(multi_index)Manipulating and Transforming Financial DataSelecting and Filtering DataEver needed to find just the right slice of market data? pandas makes it easy with .loc and .iloc:# Selecting by label print(df.loc[df['Stock'] == 'AAPL']) # Selecting by position print(df.iloc[0])Handling Missing DataMissing data is inevitable. pandas offers .fillna() and .dropna() to deal with it:df_missing = pd.DataFrame({'Stock': ['AAPL', 'MSFT', 'GOOG'], 'Price': [150, np.nan, 2800]}) print(df_missing.fillna(df_missing.mean()))Financial Market Analysis with pandasCalculating Asset ReturnsUnderstanding stock returns is crucial. You can compute daily returns using .pct_change():prices = pd.Series([100, 105, 103, 110, 120]) returns = prices.pct_change() print(returns)Measuring VolatilityVolatility reflects price fluctuations — critical for risk assessment:volatility = returns.std() print("Volatility:", volatility)Generating a Cumulative Return SeriesCumulative returns show total performance over time:cumulative_returns = (1 + returns).cumprod() print(cumulative_returns)Resampling and Aggregating Time Series DataFinancial analysts often need to adjust time frames, say, from daily to weekly data:date_rng = pd.date_range(start='2023–01–01', periods=30, freq='D') data = pd.DataFrame({'Date': date_rng, 'Price': np.random.randn(30) * 5 + 100}) data.set_index('Date', inplace=True) # Resample to weekly frequency weekly_data = data.resample('W').mean() print(weekly_data)Applying Custom Functions to Time Series DataWant a moving average? pandas makes it simple:def moving_average(series, window=3): return series.rolling(window=window).mean() # Apply a moving average function data['MA'] = moving_average(data['Price']) print(data)Conclusionpandas is an indispensable tool for financial data analysis. From handling missing data to calculating returns and volatility, it provides everything you need to extract meaningful insights from market data. Now that you’ve got the fundamentals down, you can confidently explore more advanced financial analytics, perhaps even integrating machine learning models for deeper insights.Elevate Your Algorithmic Trading GameThis article is based onPython for Algorithmic Trading CookbookbyJason Strimpel, a detailed guide to designing, building, and deploying algorithmic trading strategies using Python. Whether you’re a trader, investor, or Python developer, this book equips you with hands-on recipes to acquire, visualize, and analyze market data, design and backtest trading strategies, and deploy them in a live environment.If you’re ready to take your algorithmic trading skills to the next level, this book is your roadmap. Get your hands on a copy and start building smarter, more efficient trading strategies today!Get your copy here 👉Python for Algorithmic Trading Cookbook🔍 Fresh Insights from OpenAI ⋆✴︎˚｡⋆❯❯❯❯ Sharing the latest Model Spec: OpenAI updated its Model Spec, reinforcing customizability, transparency, and intellectual freedom while maintaining safety. Now open-sourced under CC0, it prioritizes user control, objectivity, and harm prevention. OpenAI will iteratively refine it, using real-world prompts and public feedback for improvements.❯❯❯❯ Shaping the future of retail with AI: This blog highlights Wayfair’s AI-driven transformation across ecommerce, operations, and employee training. CTO Fiona Tan discusses AI’s role in personalization, supply chain optimization, marketing, and modernizing legacy systems. Wayfair leverages ChatGPT and OpenAI APIs to enhance customer experience, automate tasks, and drive innovation.❯❯❯❯ Using AI to focus on the big picture: This blog explores how Fanatics Betting and Gaming integrates AI into finance and operations. CFO Andrea Ellis discusses AI-driven efficiency, including automating vendor identification, accelerating data analysis, and improving strategic decision-making. The company fosters broad AI adoption through structured training, task forces, and custom GPT development.❯❯❯❯ Using OpenAI o1 for financial analysis: This blog highlights how Rogo leverages OpenAI’s models to transform financial research and workflows for investment banks, private equity, and asset managers. By fine-tuning GPT-4o and o1 models, Rogo delivers real-time insights, automates diligence, and optimizes financial decision-making, saving analysts 10+ hours weekly.❯❯❯❯ Introducing the Intelligence Age: This blog explores how AI, like past groundbreaking innovations, is transforming human potential. OpenAI highlights ChatGPT’s rapid adoption, its role in education, science, medicine, and government, and its Super Bowl ad showcasing AI’s historical significance. The goal: ensuring AGI benefits everyone, driving progress and creativity.❯❯❯❯ OpenAI partners with Schibsted Media Group: This blog announces OpenAI’s partnership with Schibsted Media Group, integrating content from VG, Aftenposten, Aftonbladet, and Svenska Dagbladet into ChatGPT for up-to-date news summaries with attribution. The collaboration supports quality journalism, AI-driven content innovation, and new commercial opportunities in digital media.🚀 Trendspotting: What's Next in Tech Trends❯❯❯❯ Method of Moments Estimation with Python Code: This blog explains Method of Moments (MoM) estimation, a statistical technique for inferring probability distributions from data. Using Python code, it demonstrates MoM for Poisson and Normal distributions, showing how to estimate parameters efficiently and compare estimated distributions to true distributions for validation.❯❯❯❯ Pandas Can’t Handle This: How ArcticDB Powers Massive Datasets. This blog explores how ArcticDB outperforms Pandas for massive datasets, enabling faster data processing, better memory management, and scalability. It demonstrates ArcticDB’s use in finance and climate research, compares performance with Pandas, and highlights when to use ArcticDB for large-scale, production-ready analytics.❯❯❯❯ 7 Tools You Cannot Live Without as a Data Scientist: This blog highlights seven essential tools for data scientists, focusing on AI-driven and productivity-enhancing software. It covers Google Workspace for organization, You.com for research, Cursor for coding, and Grammarly for writing assistance, showcasing how these tools improve efficiency, workflow, and project management.❯❯❯❯ Microsoft Research and Physics Wallah team up to enhance AI-based tutoring: This blog explores Microsoft Research’s collaboration with Physics Wallah to enhance AI-powered tutoring in India using GPT-4o and advanced reasoning models. It highlights AI Guru, Smart Doubt Engine, and AI Grader, improving student accessibility, affordability, and learning outcomes for competitive exam preparation.❯❯❯❯ Meta AI Introduces CoCoMix: A Pretraining Framework Integrating Token Prediction with Continuous Concepts. This blog introduces Meta AI’s CoCoMix, an alternative LLM pretraining approach that integrates token prediction with concept-based reasoning using Sparse Autoencoders (SAEs). CoCoMix improves sample efficiency, generalization, knowledge transfer, and interpretability, offering a more structured and transparent model training paradigm.❯❯❯❯ Google DeepMind Research Introduces WebLI-100B: Scaling Vision-Language Pretraining to 100 Billion Examples for Cultural Diversity and Multilingualit. This blog discusses Google DeepMind’s WebLI-100B dataset, which scales vision-language model training to 100 billion image-text pairs. The dataset improves cultural diversity, multilinguality, and inclusivity, though traditional benchmarks show limited gains. It highlights bias challenges, dataset filtering trade-offs, and future research directions for more balanced AI models.🛠️ Platform Showdown: Comparing ML Tools & Services❯❯❯❯ Data Science Showdown: Which Tools Will Gain Ground in 2025: This blog forecasts emerging data science tools for 2025, highlighting PySpark, Numba, and Julia for big data computing, D3.js and Plotly for visualization, Streamlit, MLflow, and H2O.ai for model deployment, and OpenRefine for data preparation. It also discusses Google Cloud Platform’s growing prominence in AI and big data.❯❯❯❯ Building Multilingual Applications with Hugging Face Transformers: This blog explores building multilingual applications using Hugging Face Transformers, showcasing pre-trained models like XLM-R and mBERT for sentiment analysis, question answering, and summarization. It provides Python code, fine-tuning steps, and deployment tips to make multilingual AI integration easier and scalable.❯❯❯❯ Build a dynamic, role-based AI agent using Amazon Bedrock inline agents: This blog explores building dynamic AI assistants using Amazon Bedrock inline agents, enabling real-time tool selection, adaptive configurations, and role-based personalization. It showcases an HR assistant example, demonstrating flexibility in AI workflows, cost optimization, and scalability without managing multiple agent configurations.❯❯❯❯ Announcing Gen AI Toolbox for Databases: This blog announces the public beta of Gen AI Toolbox for Databases, an open-source server that connects agent-based generative AI applications to databases. Built in partnership with LangChain, it enhances scalability, security, observability, and tool management, supporting PostgreSQL, MySQL, and Google Cloud databases for seamless AI-driven data access.❯❯❯❯ Anthropic AI Launches the Anthropic Economic Index: A Data-Driven Look at AI's Economic Role. This blog introduces the Anthropic Economic Index, a data-driven initiative tracking AI’s economic impact across industries. Analyzing millions of anonymized Claude conversations, it maps AI adoption by occupation, highlighting high usage in software, writing, and analytical tasks while noting limited adoption in physical labor and specialized fields.❯❯❯❯ Meet Huginn-3.5B: A New AI Reasoning Model with Scalable Latent Computation. This blog introduces Huginn-3.5B, an AI model leveraging recurrent depth reasoning to dynamically refine computations in latent space. Unlike traditional Chain-of-Thought methods, it scales compute per task, improves efficiency without large context windows, and outperforms larger models in reasoning benchmarks.📊 Success Stories: Real-World ML Case Studies❯❯❯❯ 5 LLM Prompting Techniques Every Developer Should Know: This blog explores five essential LLM prompting techniques for developers: zero-shot, few-shot, chain-of-thought, tree-of-thought, and self-consistency prompting. These methods improve AI accuracy, reasoning, and response consistency, helping developers optimize interactions with models like Claude AI and ChatGPT for better results.❯❯❯❯ 10 Little-Known Python Libraries That Will Make You Feel Like a Data Wizard: This blog introduces 10 lesser-known Python libraries that enhance data science workflows, covering visualization (Altair), SQL analytics (DuckDB), geospatial analysis (H3), automated profiling (Ydata Profiling), dependency management (Poetry), graph analysis (NetworkX), scalable ML (H2O.ai, PyCaret), and missing data visualization (Missingno).❯❯❯❯ Should Data Scientists Care About Quantum Computing? This blog explores the relevance of quantum computing for data scientists, addressing how AI accelerates quantum advancements and how quantum computing could optimize data science workflows in the future. While not yet essential for most data scientists, being quantum-aware offers a strategic advantage as the field evolves.❯❯❯❯ ExACT: Improving AI agents’ decision-making via test-time compute scaling. This blog introduces Microsoft’s ExACT, a new AI framework that enhances autonomous agents' decision-making through Reflective-MCTS (R-MCTS) and Exploratory Learning. ExACT improves reasoning, adaptability, and generalization in dynamic environments, achieving state-of-the-art performance in AI benchmarks like VisualWebArena and OSWorld.❯❯❯❯ 3 Ways to Secure Your Data Science Job From Layoffs in 2025: This blog outlines three key strategies to secure your data science job amid growing AI-driven layoffs in 2025. It emphasizes the importance of strong foundational skills, business-facing roles, and workplace visibility to stay competitive and thrive in an evolving job market.❯❯❯❯ Meet OpenThinker-32B: A State-of-the-Art Open-Data Reasoning Model. This blog introduces OpenThinker-32B, an open-data reasoning model designed to excel in mathematics, coding, and scientific inquiry. Fine-tuned from Qwen2.5-32B-Instruct and trained on the OpenThoughts-114k dataset, it achieves state-of-the-art performance across reasoning benchmarks, offering an open-source alternative for AI research and development.We’ve got more great things coming your way, see you soon!*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}

Hands-On Machine Learning with C++, Vertex AI Gen AI Evaluation Service, Biostatistics with Python🌟Share, Shape, & Claim Your Free Packt Credit! 📚We're looking for data professionals to join a quick 30-minute chat about their learning needs. The first 25 respondents in a data-specific role will have the opportunity to speak with our team, share their insights, and receive a free Packt credit to claim any eBook of their choice! Hurry – submit your interest now and keep an eye out for our team's meeting invite. You could be one of the chosen ones!👉 Reserve Your Interview SlotHyperproof's 6th Annual IT Risk and Compliance Benchmark Report ReleasedGRC is no longer just a checkbox, it’s a competitive advantage.Hyperproof’s6th Annual IT Risk & Compliance Benchmark Reportreveals a major shift: organizations are maturing their GRC practices, centralizing teams, and increasing budgets. With91% of companies now prioritizing compliance, the landscape is evolving fast.The key takeaway?Governance, risk, and compliance are now drivers of operational excellence and strategic growth. Hyperproof’s industry insights and newGRC Maturity Modelequip organizations to stay ahead.📊Get thefull report& start building a stronger, more resilient GRC strategy today.Download the Report Now!Sponsored📢 Welcome to DataPro #126 ~ Your Weekly Dose of Data Science & ML Innovation!The world of data science and machine learning is advancing at lightning speed, and we’re here to keep you ahead of the curve! Whether it’s breakthrough AI frameworks, game-changing open-source tools, or must-know industry updates, this edition packs everything you need to stay informed, innovate, and lead in the ML space. 📚 New Releases You Can't Miss:✅Hands-On Machine Learning with C++ - Build smart models with modern C++ libraries.✅Biostatistics with Python - Apply Python to real-world biomedical & biotech projects.✅Data Engineering with Databricks Cookbook - Master Apache Spark, Delta Lake & Databricks.🔍 This Week’s Deep Dive:✅ Support Vector Machine (SVM) Algorithm - A fundamental yet powerful ML technique.✅ OpenAI’s Deep Research Agent -How it’s revolutionizing data-driven discovery.✅ Yandex’s Open-Source Perforator - Optimizing server performance like never before.✅ Meta AI’s MILS - A training-free multimodal AI framework pushing zero-shot learning to new heights.✅ No-Code ML with Amazon SageMaker Canvas - Predict heart disease with an intuitive workflow.✅ Vertex AI Gen AI Evaluation Service - A smarter way to assess and improve AI agents.🧠 Featured Insights:✅Mistral AI Releases Mistral-Small-24B-Instruct-2501 - A low-latency 24B-parameter model under Apache 2.0.✅Improving Agent Systems & AI Reasoning - Smarter, more reliable AI solutions.Whether you’re a data scientist, ML engineer, or AI enthusiast, DataPro keeps you informed, inspired, and ahead of the curve. Stay tuned for more updates next week!💡 Got a topic you'd love to see covered? Let us know! 🚀Cheers,Merlyn ShelleyGrowth Lead, Packt.📚 Packt Signature Series: New Releases You Can't Miss❯❯❯❯ Hands-On Machine Learning with C++:Written by Kirill Kolodiazhnyi, this book equips machine learning engineers with practical ML and deep learning techniques using modern C++ libraries. You will learn about model selection, tuning, and deployment on mobile and embedded devices, real-time object detection, transfer learning, MLflow for experiment tracking, and Optuna for hyperparameter tuning, providing a complete guide to building efficient ML systems. Start your free trial for access, renewing at $19.99/month.eBook $27.98 $39.99Print + eBook $49.99❯❯❯❯ Biostatistics with Python: Written by Darko Medin, this book simplifies biostatistics with Python through hands-on biomedical and biotechnology projects. You will learn about data cleaning, hypothesis testing, effect size analysis, predictive modeling, survival analysis, and meta-analysis, making it easier to apply statistical methods in biological research. With real-world case studies, this guide helps life science professionals and researchers confidently integrate biostatistical analysis into their work. Start your free trial for access, renewing at $19.99/month.eBook $18.99 $27.99Print + eBook $34.99❯❯❯❯ Data Engineering with Databricks Cookbook: Written by Pulkit Chadha, this cookbook provides a practical, recipe-based guide to mastering data engineering with Databricks, Apache Spark, and Delta Lake. You will learn about data ingestion, transformation, and optimization, as well as orchestrating pipelines, implementing DataOps/DevOps, and enforcing data governance with Unity Catalog. Designed for data engineers and practitioners, this book offers hands-on techniques to build scalable, high-performance data solutions in modern cloud environments. Start your free trial for access, renewing at $19.99/month.eBook $27.98 $39.99Print + eBook $49.99🔍 Fresh Insights, Trending Now on Medium ⋆✴︎˚｡⋆❯❯❯❯ Support Vector Machines: A Progression of Algorithms: This blog explores the Support Vector Machine (SVM) algorithm, a powerful tool for classification problems. It explains the progression from the Maximal Margin Classifier (MMC) to the Support Vector Classifier (SVC) and finally to SVM, highlighting how each step improves decision boundary flexibility and robustness.❯❯❯❯ Are Public Agencies Letting Open-Source Software Down? This blog explores the impact of open-source software on technology, innovation, and democracy. It highlights its role in AI advancements, geospatial mapping, and public collaboration. Through personal anecdotes and practical examples, it underscores how open access, transparency, and shared knowledge drive progress across industries and global communities.❯❯❯❯ Improving Agent Systems & AI Reasoning: This blog explores the rise of AI Agents and the limitations of large language models (LLMs) in reasoning. It examines how new Reasoning Language Models (RLMs), like DeepSeek-R1 and OpenAI’s o1 and o3, improve AI reasoning through post-training and test-time compute scaling, reshaping AI agent development.❯❯❯❯ What OpenAI’s Deep Research Means for the Future of Data Science? This blog introduces OpenAI’s Deep Research Agent, a tool designed to streamline complex data gathering and analysis for data scientists. It automates multi-step research, synthesizes information from diverse sources, ensures accuracy with verified citations, and enhances efficiency in problem-solving across domains like healthcare, finance, and AI development.🚀 Trendspotting: What's Next in Tech Trends❯❯❯❯ Meta AI Introduces MILS: A Training-Free Multimodal AI Framework for Zero-Shot Image, Video, and Audio Understanding. This blog introduces Meta AI’s MILS, a training-free multimodal AI framework that enables large language models (LLMs) to perform image, video, and audio reasoning without task-specific training. Using an iterative optimization process with a generator and scorer, MILS enhances zero-shot performance across diverse modalities, improving multimodal AI adaptability.❯❯❯❯ 4 Open-Source Alternatives to OpenAI’s $200/Month Deep Research AI Agent. This blog explores four open-source AI research agents that serve as cost-effective alternatives to OpenAI’s Deep Research AI Agent. These tools leverage advanced search, extraction, and reasoning capabilities, offering researchers customizable, self-hostable solutions for automating in-depth research without the high cost of proprietary AI systems.❯❯❯❯ Mistral AI Releases the Mistral-Small-24B-Instruct-2501: A Latency-Optimized 24B-Parameter Model Released Under the Apache 2.0 License: This blog introduces Mistral-Small-24B-Instruct-2501, a compact yet high-performing language model designed for efficiency and accessibility. With 24 billion parameters, multilingual capabilities, and a 32k context window, it rivals larger models like Llama 3 while supporting local deployment and open-source flexibility under the Apache 2.0 license.❯❯❯❯ Yandex Develops and Open-Sources Perforator: An Open-Source Tool that can Save Businesses Billions of Dollars a Year on Server Infrastructure. This blog introduces Perforator, an open-source tool from Yandex designed for real-time server and application performance monitoring. By identifying resource-intensive code and enabling profile-guided optimization, Perforator helps businesses cut infrastructure costs by up to 20%, making it a powerful solution for efficiency and scalability.🛠️ Platform Showdown: Comparing ML Tools & Services❯❯❯❯ Advances to low-bit quantization enable LLMs on edge devices: This blog explores advancements in low-bit quantization for deploying large language models (LLMs) on edge devices. Microsoft Research introduces T-MAC, Ladder, and LUT Tensor Core, three solutions optimizing mixed-precision matrix multiplication (mpGEMM) to improve AI efficiency. These innovations enhance model performance, reduce memory demands, and enable real-time AI processing on resource-constrained hardware.❯❯❯❯ Trellix lowers cost, increases speed, and adds delivery flexibility with cost-effective and performant Amazon Nova Micro and Amazon Nova Lite models: This blog explores how Trellix Wise, an AI-powered cybersecurity platform, integrates Amazon Nova Micro to enhance threat investigation speed and cost efficiency. By leveraging generative AI and Retrieval-Augmented Generation (RAG), Trellix automates security event analysis, reducing investigation time while maintaining accuracy, improving scalability, and optimizing operational costs.❯❯❯❯ OfferUp improved local results by 54% and relevance recall by 27% with multimodal search on Amazon Bedrock and Amazon OpenSearch Service: This blog explores how OfferUp modernized its search architecture by adopting Amazon Titan Multimodal Embeddings and Amazon OpenSearch Service. By integrating multimodal search, OfferUp improved search relevance, user engagement, and local discovery, enabling users to search with both text and images for a more intuitive marketplace experience.❯❯❯❯ Use generative AI on AWS for efficient clinical document analysis: This blog explores how Clario leverages generative AI on AWS to streamline clinical trial document analysis. By integrating Amazon Textract, OpenSearch, Bedrock, and SageMaker, Clario automates parsing, retrieval, classification, and analysis, significantly reducing review time and accelerating drug development while maintaining regulatory compliance.❯❯❯❯ Build a multi-interface AI assistant using Amazon Q and Slack with Amazon CloudFront clickable references from an Amazon S3 bucket: This blog explores how Amazon Q Business and Slack enable multi-interface AI assistants for seamless user interaction. By integrating Retrieval Augmented Generation (RAG) with Amazon Kendra and CloudFront, organizations can enhance AI accessibility, provide context-aware responses, and improve productivity without requiring users to switch applications.📊 Success Stories: Real-World ML Case Studies❯❯❯❯ No-Code ML Approach to Predict Heart Disease with Amazon SageMaker Canvas: This blog explores how Amazon SageMaker Canvas enables no-code predictive modeling for heart disease detection. By integrating SageMaker Data Wrangler for data preparation and machine learning for classification, healthcare professionals can analyze biomedical data, identify key indicators, and improve early diagnosis without extensive coding expertise.❯❯❯❯ OpenAI Introducing data residency in Europe: This blog introduces data residency in Europe for ChatGPT Enterprise, ChatGPT Edu, and the API Platform, enhancing data sovereignty compliance for organizations. OpenAI ensures secure, private AI usage with in-region data processing, encryption, and GDPR compliance, empowering businesses and institutions across Europe to integrate AI confidently.❯❯❯❯ Create a 360-degree master data management patient view solution using Amazon Neptune and generative AI: This blog explores how Amazon Neptune and generative AI enable a 360-degree patient view, integrating electronic health records (EHRs), lab results, prescriptions, and social determinants. By unifying healthcare data, providers can enhance personalized care, improve early disease detection, and support clinical decision-making, leading to better patient outcomes.❯❯❯❯ Build a brand logo with Imagen 3 and Gemini: This post explores how Imagen 3, Gemini, and the Python Library Pillow work together to help businesses create branded marketing visuals. Using AI-powered image generation, selection, and integration, companies can design unique brand identities and logos tailored to their aesthetic. Learn how this AI workflow can enhance your creative process and deliver high-quality promotional visuals efficiently.❯❯❯❯ Evaluate your AI agents with Vertex Gen AI evaluation service: Vertex AI Gen AI Evaluation Service is now in public preview, enabling rigorous AI agent assessment. It offers final response and trajectory analysis metrics to improve decision-making. Compatible with LangChain, LangGraph, CrewAI, and Google Cloud services, it supports native agent inference and automatic logging in Vertex AI Experiments.We’ve got more great things coming your way, see you soon!*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}

TensorLLM, Vertex AI RAG Engine, Qwen2.5-MaxFortified’s Central Command Platform Named “Healthcare Cybersecurity Solution of the Year”Fortified Health Security's Central Command platform has won the "Healthcare Cybersecurity Solution of the Year" at the CyberSecurity Breakthrough Awards! This unified platform simplifies cybersecurity for healthcare organizations by integrating Advisory Services and Threat Defense (SOC). With real-time insights, mobile alerts, and a Risk Register, it empowers healthcare providers to manage risks efficiently, mitigate threats, and protect patients. Stay ahead of the threats. Explore Central Command today.Learn more and see it in actionSponsored🗞️Welcome toDataPro #125– Your Weekly Data Science & ML Wizardry! 🌟We are back from the holiday break! We hope you've missed our updates as much as we've missed sharing them with you. 😊We’ve been working on something exciting to make your learning journey even easier, and we’d love for you to help co-create it with us!Before we dive in, take a quick moment to fill out our survey. As a thank you, we’ll give you access to a free AI Crash Course eBook!Now, let’s jump into this week's exciting updates:📚 New Releases You Can't Miss:✦ Causal Inference in R✦ Python Feature Engineering Cookbook✦ Quantum Machine Learning and Optimisation in Finance🔍 Fresh Insights:✦ Wake Vision: Solving the TinyML Dataset Crisis✦ Microsoft’s CoRAG: Raising the Bar for Data Science✦ DeepSeek-R1: Advancing Reasoning and Affordability✦ Meta AI Launches MR.Q: Revolutionizing Reinforcement Learning🚀 Trendspotting:✦ Coding with Qwen 2.5✦ 10 Advanced Python Tricks for Data Scientists✦ ChatGPT Gov - OpenAI✦ Vertex AI RAG EngineStay on Top of the DS & ML World with Innovative Tools, Insights, and Strategies. This week, we’ve gathered trending resources to fine-tune your projects and ignite your next breakthrough. Let’s go!Design the Learning Journey You Want!🌟Help Us Make Your Learning Journey Even Better!🌟As we mentioned earlier, we've got something exciting in the works to make your experience with Data Science, BI, and ML even easier, and we’d absolutely love forYOUto be a part of it!Your input will help us create the perfect learning experience for you! It’ll only take a few minutes, and as a thank-you, you’ll get full access to a free ebook on theAI CrashCourse!👉Take the Survey Now!Let's make learning even more amazing, together! 💡Take the Survey Now!Cheers,Merlyn ShelleyGrowth Lead, Packt.Start PII Leak Detection and Data Flow Mapping Where It Matters Most: In the Code92% of breaches in 2023 involved PII. HoundDog bridges AppSec and Data Security with an ultra-fast, lightweight static code scanner that detects PII leaks early, preventing costly fixes later.It automates compliance for frameworks like HIPAA, PCI, GDPR, and FedRAMP, ensuring PII safety from development to deployment. Trusted by Fortune 500s, HoundDog enables shift-left PII prevention with IDE plug-ins and CI/CD integration. Book a demo now to see how HoundDog can streamline your security and compliance efforts!Book a Live DemoSponsored📚 Packt Signature Series: New Releases You Can't Miss❯❯❯❯ Causal Inference in R: Written by Subhajit Das, this book offers a deep dive into causal inference using R, guiding readers through foundational concepts and advanced techniques like propensity score matching and instrumental variables. It helps you develop skills to construct and interpret causal models, address challenges in controlled experiments, and apply doubly robust estimation. With real-world case studies and hands-on examples, the book empowers readers to make informed, data-driven decisions by understanding and establishing causal relationships with precision. Start your free trial for access, renewing at $19.99/month.eBook $24.99 $35.99Print + eBook $44.99❯❯❯❯ Python Feature Engineering Cookbook: Written by Soledad Galli, this third edition of the Python Feature Engineering Cookbook provides a complete guide to crafting powerful features for machine learning models. It covers practical solutions for common challenges, such as imputing missing values and encoding categorical variables, while optimizing data transformation processes. The book explores advanced techniques like feature extraction from dates, times, text, and time series data, as well as using tools like Featuretools and tsfresh. With step-by-step instructions and real-world examples, it helps readers build reproducible feature engineering pipelines, ultimately enhancing machine learning model performance. Start your free trial for access, renewing at $19.99/month.eBook $24.99 $35.99Print + eBook $44.99❯❯❯❯ Quantum Machine Learning and Optimisation in Finance: Written by Antoine Jacquier and Oleksiy Kondratyev, this second edition of Quantum Machine Learning and Optimisation in Finance explores how quantum algorithms enhance financial modeling and decision-making. The book focuses on quantum machine learning (QML) and optimization algorithms, with an emphasis on near-term applications using NISQ systems. It offers practical insights into hybrid quantum-classical computational protocols and addresses the limitations of current quantum hardware. The authors provide an accessible yet rigorous approach to QML, covering topics like quantum neural networks, quantum annealing, and variational algorithms, equipping readers with the knowledge to apply quantum techniques in financial innovation. Start your free trial for access, renewing at $19.99/month.eBook $24.99 $35.99Print + eBook $44.99🔍 Packt on Medium: Fresh Insights, Trending Now ⋆✴︎˚｡⋆❯❯❯❯ Wake Vision: Solving the TinyML Dataset Crisis. This blog introduces Wake Vision, a dataset designed to tackle the challenges of TinyML by addressing data scarcity and quality issues. With 6 million images, it offers both large and high-quality training sets, improving model accuracy and performance across real-world conditions like distance, lighting, and bias detection.❯❯❯❯ Microsoft’s CoRAG: Raising the Bar for Accuracy and Efficiency in Data Science. This blog introduces Microsoft’s Chain-of-Retrieval Augmented Generation (CoRAG) model, a breakthrough in machine learning. CoRAG improves knowledge-intensive tasks by using multiple retrieval steps for complex queries, enhancing accuracy, efficiency, and relevance in real-world applications like customer support, healthcare, and legal analysis.❯❯❯❯ DeepSeek-R1: Advancing Reasoning and Affordability. This blog highlights DeepSeek-R1, an affordable AI model that delivers powerful performance without high costs. It excels in multi-step reasoning tasks, offering real-world applications like running AI on smartphones, chatting with PDFs, and distributed AI across devices, all while keeping API prices low.❯❯❯❯ Learn-by-Interact: Google Cloud’s Data-Centric Framework Redefining AI Agents. This blog introduces Google Cloud’s Learn-by-Interact, a revolutionary AI framework that enables autonomous learning through agent-environment interactions. By generating high-quality training data and adapting instructions based on agent experiences, it enhances performance and efficiency, eliminating the need for manual annotations.❯❯❯❯ How DeepSeek-V3 is Revolutionizing AI: A Technical Report on Solving Real-World Challenges? This blog introduces DeepSeek-V3, a powerful yet cost-effective AI model designed for businesses and developers. With features like efficient load balancing, multi-token prediction, and mixed precision training, DeepSeek-V3 offers scalable solutions for coding, scientific research, customer service, and knowledge retrieval without the high costs of traditional models.✔️ Follow us on Medium for exclusive updates and deep dives into the trends. Packt Hub – Medium🚀 Trendspotting: What's Next in Tech Trends❯❯❯❯ Don’t Manage Your Python Environments, Just Use Docker Containers: This blog explains how to manage Python environments using Docker containers to avoid dependency headaches and version conflicts. It provides a step-by-step guide for setting up a Docker-based environment, including creating a Dockerfile, building an image, and managing containers. Docker’s isolation ensures clean setups for multiple projects, allowing developers to share environments with ease.❯❯❯❯ Using DeepSeek-R1 Locally: This blog introduces DeepSeek-R1, an advanced reasoning AI model that rivals OpenAI's performance on benchmarks like MMLU and Math-500. It guides you through setting up the DeepSeek-R1 Distill version locally using Ollama, Docker, and Open WebUI. You’ll learn how to run a model with a ChatGPT-like interface, perform tasks such as code generation and logical reasoning, and access it entirely offline without relying on cloud services.❯❯❯❯ Coding with Qwen 2.5: An Overview: This blog introduces Qwen2.5, a powerful AI model series from Alibaba, designed to compete with top-tier models. It explores various applications like text generation, sentiment analysis, coding, and mathematical reasoning. The blog guides users through using Qwen2.5 locally with PyTorch, showcasing its capabilities.❯❯❯❯ Data Wrangling in Rust with Polars: This blog explores Polars, a fast, memory-efficient data wrangling library built in Rust, designed for handling large datasets. It covers essential features like data filtering, aggregation, sorting, joining, and lazy execution. Polars offers superior performance and low memory usage compared to Pandas, making it ideal for big data tasks.❯❯❯❯ 10 Advanced Python Tricks for Data Scientists: This blog introduces 10 advanced Python tricks every data professional should know, from using pandas_profiling for quick dataset summaries to applying f-strings for cleaner formatting. It also covers lambda functions, NumPy broadcasting, itertools, matplotlib subplots, and more to optimize data wrangling and machine learning workflows. These tricks will help make your code cleaner, faster, and more efficient.❯❯❯❯ Deploy DeepSeek-R1 Distilled Llama models in Amazon Bedrock: This blog explores how to deploy DeepSeek-R1 distilled models using Amazon Bedrock Custom Model Import. It highlights the DeepSeek-R1-Distill-Llama-8B and DeepSeek-R1-Distill-Llama-70B models, which offer a balance between performance and efficiency. These models, derived from the larger DeepSeek-R1 family, are more cost-effective and faster for production deployments, making them ideal for businesses using Amazon Bedrock. The article walks through importing models from Amazon S3 and deploying them in a fully managed, serverless environment, eliminating infrastructure management and ensuring scalability.🛠️ Platform Showdown: Comparing ML Tools & Services❯❯❯❯ ChatGPT Gov - OpenAI: This blog introduces ChatGPT Gov, a tailored version of ChatGPT designed to streamline U.S. government agencies' access to OpenAI’s frontier models. Hosted on Microsoft Azure, it allows agencies to manage security, privacy, and compliance requirements, enabling the handling of non-public sensitive data. ChatGPT Gov includes GPT-4, customizable GPTs, and tools for improving efficiency in government operations. It has already been adopted by over 90,000 users across 3,500 agencies, supporting tasks in areas like coding, research, and translation.❯❯❯❯ Prompting Vision Language Models. Exploring techniques to prompt VLMs: This blog explores Vision Language Models (VLMs), which combine text and image inputs for tasks like image captioning and visual question answering. It covers zero-shot, few-shot, and chain-of-thought prompting techniques, demonstrating how VLMs can analyze and generate captions for images using GPT-4o-mini.❯❯❯❯ Vertex AI RAG Engine: Build & deploy RAG implementations with your data. This blog introduces Vertex AI's RAG Engine, a fully managed service for building and deploying retrieval-augmented generation (RAG) applications. It offers flexibility with model selection, vector databases, and data sources, improving performance and scalability while ensuring high-quality, context-aware AI outputs for enterprise applications.❯❯❯❯ The Invisible Revolution: How Vectors Are (Re)defining Business Success: This article discusses the importance of vector thinking in business, explaining how vectors help uncover complex relationships in data. It highlights the benefits of understanding vector-based computing for tasks like fraud detection and customer analysis, emphasizing its role in enhancing decision-making and leveraging AI.❯❯❯❯ Build a Decision Tree in Polars from Scratch: This article explores using Polars for building a decision tree classifier from scratch. It highlights how Polars' efficient data handling, including streaming capabilities and optimized memory usage, improves decision tree training and prediction. The approach involves applying categorical mappings, target encoding, and recursive tree-building methods.❯❯❯❯ NVIDIA AI Launches Eagle2: Setting SOTA Benchmarks in Vision-Language Models. This paper discusses Eagle2, a set of vision-language models (VLMs) developed with a focus on post-training data strategies. By building these strategies from scratch, the authors highlight the importance of data-centric approaches in enhancing model performance, with Eagle2-9B achieving state-of-the-art results in multimodal benchmarks.❯❯❯❯ Qwen2.5-Max: Exploring the Intelligence of Large-scale MoE Model. This article introduces Qwen2.5-Max, a large-scale MoE model pretrained on 20 trillion tokens. It highlights Qwen2.5-Max's performance across various benchmarks, outperforming other models like DeepSeek V3, and discusses its availability via Alibaba Cloud API, along with future advancements in model intelligence.📊 Success Stories: Real-World ML Case Studies❯❯❯❯ Generative AI vs. Predictive AI: This article explores the differences between Generative AI and Predictive AI, highlighting their objectives, methodologies, and applications. Generative AI focuses on creating new data, while Predictive AI aims to forecast outcomes based on historical data. The article also discusses their convergence and real-world impact.❯❯❯❯ Beyond Open Source AI: How Bagel’s Cryptographic Architecture, Bakery Platform, and ZKLoRA Drive Sustainable AI Monetization. Bagel introduces a transformative AI architecture integrating cryptography and machine learning to foster decentralized, secure collaboration in model fine-tuning. The ZKLoRA protocol enables efficient, privacy-preserving verification of LoRA updates, ensuring scalability, intellectual property protection, and trust within decentralized AI development. Bagel’s Bakery platform monetizes contributions.❯❯❯❯ Meta AI Launches MR.Q: Redefining Reinforcement Learning for Better Generalization. MR.Q is a model-free reinforcement learning (RL) algorithm that incorporates model-based representations for improved efficiency and generalization. It achieves strong performance across various benchmarks with minimal tuning, outperforming traditional methods while maintaining computational efficiency, making it a versatile and practical solution for RL applications.❯❯❯❯ DeepSeek-AI Releases Janus-Pro 7B: An Open-Source multimodal AI that Beats DALL-E 3 and Stable Diffusion. This article discusses Janus-Pro, a refined multimodal AI model that improves on its predecessor by addressing inefficiencies in visual encoding and training. It highlights its advancements in understanding and generating both text and images, demonstrating superior performance in various benchmarks through architectural innovation and enhanced training strategies.❯❯❯❯ TensorLLM: Enhancing Reasoning and Efficiency in Large Language Models through Multi-Head Attention Compression and Tensorisation. This article introduces a framework developed by Imperial College London to compress the Multi-Head Attention (MHA) block in transformer-based large language models (LLMs). By applying multi-head tensorisation and Tucker decomposition, it enhances reasoning abilities and achieves up to 250x parameter compression, improving efficiency without additional training.❯❯❯❯ Parlant: The Open-Source Framework for Reliable AI Agents. This article introduces Parlant, an open-source AI system designed to improve chatbot performance by addressing common failures in task execution. It uses a dynamic control system with contextual evaluation, behavioral guidelines, and self-critique mechanisms, ensuring agents follow business rules, maintain coherence, and provide consistent, reliable responses.We’ve got more great things coming your way, see you soon!*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}

Podcast with Gemini 1.5 Pro, Structured Generation for LLM-as-a-Judge Evaluations, Arabic Stable LMStop worrying about your to-do list.Zapier connects the apps you use every day, so you can focus on what matters most.Start working more efficiently - Create your free account today.Get started for freeSponsored🗞️ Welcome to DataPro #124 – Your Weekly Data Science & ML Wizardry! 🌟Stay on top of the AI and ML game with cutting-edge tools, insights, and strategies. This week, we’re bringing you trending resources to supercharge your projects, enhance accuracy, and drive innovation. Let’s dive in!🔍 Algorithm Spotlight: Models Making Waves✦ Google Gemini 2.0: Ushering in the agentic AI era.✦ AlphaQubit: Google’s breakthrough in quantum error correction.✦ Genie 2: A massive foundation world model.✦ OpenAI’s GPT-4o-mini: Transforming retail experiences.✦ Microsoft's AI Carbon Tracker: Real-time global emission monitoring.✦ Quartz Atlas AI: Accelerating drug discovery.🚀 Trend Watch: What’s Hot in Tech✦ Top 5 Tips for Fine-Tuning LLMs.✦ AI Implementation Lessons from Early Adopters.✦ DeepSeek V2.5: Next-gen insights.✦ MAG-V by Splunk: AI innovation decoded.✦ Stability AI’s Arabic Stable LM 1.6B: A new language model frontier.🛠️ Tool Picks: ML Services in the Spotlight✦ 7 Python Libraries Every MLOps Pro Needs.✦ The Dark Side of Tech: Misuse in Education.✦ EXAONE 3.5 by LG AI Research: Advancing AI capabilities.✦ CePO by Cerebras: Smart planning and optimization.✦ Hugging Face TGI v3.0: Revolutionizing text generation.✦ Meta AI SPDL: Efficient data loading at scale.📊 ML in Action: Stories That Inspire✦ Gemini 1.5 Pro: Building a podcast powerhouse.✦ Text Classification 101 with Hugging Face Transformers.✦ 3 Key Business Skills for Data Science Careers in 2025.✦ LLM-as-a-Judge: Structured Generation in Practice.✦ Shopify Case Study: Using synthetic data effectively.✦ Combining Big and Small LLMs for Faster, Better Inference.✦ Building a Versatile LLM Agent: Step by Step.Enjoy exploring, learning, and building this week!Stay tuned and stay inspired – there’s always something new to discover in the ever-evolving world of Data Science and Machine Learning!Take our weekly survey and get a free PDF copy of our best-selling book,"Interactive Data Visualization with Python - Second Edition."We appreciate your input and hope you enjoy the book!Share Your Insights and Shine! 🌟💬This is our final edition of DataPro for 2024, but don’t worry—we’ll be back with more insights and updates in January 2025. In the meantime, we’ve got a little holiday treat for you!Packt has some exciting offers lined up to help you boost your tech skills and get ready for an amazing new year! It’s the perfect opportunity to relax, learn something new, and stay ahead in your field. Keep an eye out for these special holiday deals!From all of us at the Packt Newsletters team, we wish you a joyful holiday season and a fantastic start to 2025. See you next year! 🎄✨Cheers,Merlyn ShelleyEditor-in-Chief, Packt.Mastering Software Deployments at the Edge: A User’s Guide to Diverting DisasterSoftware delivery to dedicated edge devices is one of the most complex challenges faced by IT professionals today. While edge deployments come with inherent complications, it’s possible to avoid the pitfalls. With this guide in hand, a little planning, and the right tools and strategies in place, you can be confident you’ll never push a faulty update at scale.Read the GuideSponsored📚 Packt Signature Series: Must-Reads & Author Insights➽ RAG-Driven Generative AI: This new title, RAG-Driven Generative AI, is perfect for engineers and database developers looking to build AI systems that give accurate, reliable answers by connecting responses to their source documents. It helps you reduce hallucinations, balance cost and performance, and improve accuracy using real-time feedback and tools like Pinecone and Deep Lake. By the end, you’ll know how to design AI that makes smart decisions based on real-world data—perfect for scaling projects and staying competitive! Start your free trial for access, renewing at $19.99/month.eBook $24.99 $35.99Print + eBook $43.99➽ Building Production-Grade Web Applications with Supabase: This new book is all about helping you master Supabase and Next.js to build scalable, secure web apps. It’s perfect for solving tech challenges like real-time data handling, file storage, and enhancing app security. You'll even learn how to automate tasks and work with multi-tenant systems, making your projects more efficient. By the end, you'll be a Supabase pro! Start your free trial for access, renewing at $19.99/month.eBook $15.99 $31.99Print + eBook $39.99➽ Python Data Cleaning and Preparation Best Practices: This new book is a great guide for improving data quality and handling. It helps solve common tech issues like messy, incomplete data and missing out on insights from unstructured data. You’ll learn how to clean, validate, and transform both structured and unstructured data—think text, images, and audio—making your data pipelines reliable and your results more meaningful. Perfect for sharpening your data skills! Start your free trial for access, renewing at $19.99/month.eBook $24.99 $35.99Print + eBook $44.99🔍 Model Breakdown: Unveiling the Algorithm of the Week➽ Google introduces Gemini 2.0: A new AI model for the agentic era. Google has introduced Gemini 2.0, its most advanced AI model yet, with groundbreaking multimodal capabilities, agentic features for enhanced reasoning, and integration across products like Search. It’s faster, smarter, and redefines AI’s role as a universal assistant.➽ AlphaQubit: Google’s research on quantum error correction. Google DeepMind and Quantum AI introduce AlphaQubit, a groundbreaking AI decoder that improves quantum error correction with unmatched accuracy. This innovation brings us closer to reliable quantum computing, unlocking possibilities in drug discovery, material design, and fundamental science.➽ Genie 2: A large-scale foundation world model. Google DeepMind unveils Genie 2, a cutting-edge world model generating endless 3D environments for training AI and interactive gameplay. From a single image prompt, it creates action-controllable worlds, accelerating embodied agent development and advancing AI research.➽ Boosting the customer retail experience with GPT-4o-mini: Zalando, Europe’s leading online fashion platform, partnered with OpenAI to enhance its AI-powered Zalando Assistant. Upgraded to GPT-4o mini, the Assistant now delivers personalized recommendations in 25 markets, boosting product clicks by 23%, wishlists by 41%, and reducing costs.➽ Microsoft Research Introduces AI-Powered Carbon Budgeting Method: A Real-Time Approach to Tracking Global Carbon Sinks and Emission. Microsoft Research Asia, in collaboration with global institutions, introduces an AI-powered method for near-real-time carbon budgeting. Using satellite data and machine learning, the model predicts global carbon sinks with unprecedented speed and accuracy, addressing critical climate change challenges.➽ Quartz Atlas AI for Drug Discovery: Quartz Atlas AI™, developed by Deloitte and AWS, revolutionizes drug discovery by streamlining data connectivity, enhancing insights with domain-specific AI models, and simplifying accessibility for researchers. This AI-powered workbench accelerates R&D while reducing reliance on costly, unproductive trials.🚀 Trendspotting: What's Next in Tech Trends➽ Top 5 Tips for Fine-Tuning LLMs: Fine-tuning large language models (LLMs) can unlock domain-specific performance for tasks in medicine, law, and beyond. By prioritizing data quality and selecting the right architecture, like GPT for generation or BERT for comprehension, models become more robust and effective.➽ Overcoming AI Implementation Challenges: Lessons from Early Adopters. Implementing AI is transformative but challenging, with hurdles like data quality, accessibility, and talent shortages. Early adopters share valuable lessons in overcoming these issues, emphasizing robust data management, scalable infrastructure, and fostering skilled talent for successful AI adoption.➽ DeepSeek AI Just Released DeepSeek-V2.5-1210: DeepSeek AI introduces DeepSeek-V2.5-1210, an enhanced model excelling in mathematics, coding, writing, and reasoning. With improved accuracy, live coding capabilities, and user-friendly features, it’s a versatile tool for researchers, developers, and professionals across diverse fields.➽ Splunk Researchers Introduce MAG-V: Splunk Inc. introduces MAG-V, a multi-agent framework addressing challenges in AI trajectory verification and synthetic data generation. By combining machine learning and deterministic methods, MAG-V ensures accuracy, scalability, and privacy while outperforming traditional LLM-based solutions in reliability and cost-efficiency.➽ Stability AI Releases Arabic Stable LM 1.6B Base and Chat Models: Stability AI's Arabic Stable LM 1.6B offers a resource-efficient solution for Arabic NLP, balancing cultural alignment and performance. With fine-tuning on over 100 billion tokens, it excels in tasks like question answering and cultural context recognition, advancing inclusivity in language AI.🛠️ Platform Showdown: Comparing ML Tools & Services➽ 7 Essential Python Libraries for MLOps: This blog explores seven essential Python libraries for MLOps, enabling users to streamline machine learning workflows, from experiment tracking and orchestration to model serving and performance monitoring, with tools like MLflow and Prefect.➽ Accusatory AI: How misuse of technology is harming students. This blog discusses the flaws of AI-powered cheating detection tools in education, highlighting their potential for false accusations against students. It emphasizes the importance of transparency, evidence, and fairness, urging educators to use these tools constructively rather than as punitive measures.➽ LG AI Research Releases EXAONE 3.5: LG AI Research's EXAONE 3.5 introduces advanced bilingual models excelling in English and Korean tasks, offering long-context processing, scalability, and cost-efficiency. With three versions optimized for diverse applications, EXAONE 3.5 sets new benchmarks in language AI performance.➽ Cerebras Introduces CePO (Cerebras Planning and Optimization): Cerebras introduces CePO, an AI framework enhancing Llama models with embedded planning and reasoning capabilities. CePO streamlines complex decision-making in industries like logistics and healthcare, combining neural-symbolic methods for adaptability, efficiency, and scalability in advanced optimization tasks.➽ Hugging Face Releases Text Generation Inference (TGI) v3.0: Hugging Face's Text Generation Inference (TGI) v3.0 enhances text generation efficiency, offering 13x faster processing, 3x higher token capacity, and reduced memory usage. It simplifies deployment with zero-configuration, enabling scalable, high-performance NLP for long prompts and dynamic contexts.➽ Meta AI Introduces SPDL (Scalable and Performant Data Loading): Meta AI's SPDL (Scalable and Performant Data Loading) optimizes AI training by accelerating data delivery to GPUs. With thread-based architecture, prefetching, and caching, SPDL reduces training times, cuts costs, and boosts efficiency, making it ideal for large-scale, distributed AI workflows.📊 Success Stories: Real-World ML Case Studies➽ Learn how to build a podcast with Gemini 1.5 Pro: Google Cloud's Gemini 1.5 Pro and Text-to-Speech API enable creators to generate custom podcasts by transforming written content into engaging audio formats. With diverse voices, multilingual support, and script generation, this approach expands reach, boosts engagement, and repurposes content effortlessly.➽ How to Build a Text Classification Model with Hugging Face Transformers? This article explains how to train a transformer-based text classification model using Hugging Face Transformers in five simple steps. It covers loading data, tokenizing, initializing model architecture, and fine-tuning with ease for custom tasks.➽ 3 Business Skills You Need to Progress Your Data Science Career in 2025: This blog highlights the essential business and strategic skills data scientists need as they transition into leadership roles. It emphasizes the importance of financial fluency, staying updated on AI/ML trends, and aligning technical expertise with business impact for career growth.➽ How to Use Structured Generation for LLM-as-a-Judge Evaluations? This blog explores the concept of structured generation, a method to guide large language model (LLM) outputs into specific formats using schemas like context-free grammars (CFG). It demonstrates how structured generation enhances tasks such as hallucination detection and content validation in LLM-based evaluations.➽ Synthetic Data in Practice: A Shopify Case Study: This blog examines the practical utility of synthetic data through a side-by-side comparison of 30,000 real Shopify transactions and their synthetic counterparts. It evaluates how closely synthetic data mirrors real trends, identifies discrepancies, and highlights when it’s reliable for decision-making.➽ Combining Large and Small LLMs to Boost Inference Time and Quality: This blog explores efficient and high-quality text generation strategies using contrastive decoding, combining large and small language models. It demonstrates how optimizing token selection improves inference speed and output reliability in large language models like GPT-2.➽ How to Build a General-Purpose LLM Agent? This blog explains how to build a general-purpose LLM agent, a versatile system capable of executing user queries with adaptable workflows. It covers selecting the right LLM, defining agent control logic, and leveraging agentic architectures for diverse, flexible use cases.We’ve got more great things coming your way—see you soon!*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}

Univariate Exemplar Recommenders, PostgreSQL Optimization, Run-Time Strategies for Next-Gen Models👋 Hello ,🗞️Welcome to DataPro #123 – Your Weekly Data Science & ML Wizardry! 🌟Keep up with the latest AI and ML insights, tools, and strategies to power up your projects. This week, we’ve curated the most exciting updates and resources to sharpen your skills and boost your results. Let’s jump in!🧠 Algorithm Spotlight: Unlock the Tech Behind the Magic◘ Veo and Imagen 3 on Vertex AI: Explore cutting-edge generative models.◘ MarS Engine: Unified simulation for financial markets with generative AI.◘ Run-Time Strategies for Next-Gen Models: A peek into advanced methods.◘ MatterSimV1-1M & V1-5M: Microsoft’s latest open-source tools for AI research.◘ Meet MegaParse: Open-source tool to prep documents for large language models.◘ Promptwright by Stacklock: Create synthetic datasets with LLMs.◘ Amazon Nova: High-performance foundation models for transformative AI.🚀 Hot Trends: What’s Buzzing in AI & ML?◘ Gemini for Restaurants: AI-driven operational insights for eateries.◘ ML in Legacy Systems: Seamlessly integrate AI into your software.◘ The Void IDE: Open-source AI for coding with precision.◘ Top 10 Reinforcement Learning Repos: Master the art of RL.◘ Python Tips: Tackle large datasets like a pro.◘ Cross-Lingual Transfer: mBERT tricks for multilingual tasks.◘ Amazon SageMaker Lakehouse: Simplify enterprise data management.🛠️ Tools of the Trade: Pick the Best for Your Projects◘ Fireworks.ai: Efficiency-first generative AI engine.◘ Amazon Q Developer: Modernize mainframes with generative agents.◘ Matrix Transformations Explained: A guide to interpreting matrix math.◘ Univariate Exemplar Recommenders: Customer profiling, simplified.◘ SQL vs. Calculators: DIY champion/challenger tests.◘ Google Colab Tips: Train language models with ease.◘ PostgreSQL Optimization: Smarter queries for everyday use.📊 Real Wins: Learning from Case Studies◘ Data Science Journeys: Lessons from experienced practitioners.◘ RAG Systems: Exploring Retrieval-Augmented Generation.◘ Prompt Engineering Expertise: Build skills that matter.◘ ML Experiments Done Right: Best practices for experimentation.◘ Model Validation: Techniques for robust evaluations.◘ Explainable Recommendations: Making AI in news more transparent.◘ Enterprise AI Chatbots: Why they fail and how to fix them.Enjoy exploring, learning, and building this week!Stay tuned and stay inspired – there’s always something new to discover in the ever-evolving world of Data Science and Machine Learning!Take our weekly survey and get a free PDF copy of our best-selling book,"Interactive Data Visualization with Python - Second Edition."We appreciate your input and hope you enjoy the book!Share Your Insights and Shine! 🌟💬Cheers,Merlyn Shelley,Editor-in-Chief, Packt.Learn Million Dollar AI Strategies & Tools in this 3 hour AI Training for Free.This 3 hour power packed workshop that will teach you 30+ AI Tools, make you a master of prompting & talk about hacks, strategies & secrets that only the top 1% know of.By the way, here’s sneak peek into what’s inside the training:- Making money using AI 💰- The latest AI developments, like GPT o1 🤖- Creating an AI clone of yourself, that functions exactly like YOU 🫵- 10 BRAND new AI tools to automate your work & cut work time by 50% ⏱️1.5 Million people are already RAVING about this hands-on Training on AI Tools. Don’t take our word for it? Attend for yourself and see.Register here (first 100 people get it for free + $500 bonus) 🎁Sponsored📚 Packt Signature Series: Must-Reads & Author Insights➽ RAG-Driven Generative AI: This new title, RAG-Driven Generative AI, is perfect for engineers and database developers looking to build AI systems that give accurate, reliable answers by connecting responses to their source documents. It helps you reduce hallucinations, balance cost and performance, and improve accuracy using real-time feedback and tools like Pinecone and Deep Lake. By the end, you’ll know how to design AI that makes smart decisions based on real-world data—perfect for scaling projects and staying competitive! Start your free trial for access, renewing at $19.99/month.eBook $24.99 $35.99Print + eBook $43.99➽ Building Production-Grade Web Applications with Supabase: This new book is all about helping you master Supabase and Next.js to build scalable, secure web apps. It’s perfect for solving tech challenges like real-time data handling, file storage, and enhancing app security. You'll even learn how to automate tasks and work with multi-tenant systems, making your projects more efficient. By the end, you'll be a Supabase pro! Start your free trial for access, renewing at $19.99/month.eBook $15.99 $31.99Print + eBook $39.99➽ Python Data Cleaning and Preparation Best Practices: This new book is a great guide for improving data quality and handling. It helps solve common tech issues like messy, incomplete data and missing out on insights from unstructured data. You’ll learn how to clean, validate, and transform both structured and unstructured data—think text, images, and audio—making your data pipelines reliable and your results more meaningful. Perfect for sharpening your data skills! Start your free trial for access, renewing at $19.99/month.eBook $24.99 $35.99Print + eBook $44.99🔍 Model Breakdown: Unveiling the Algorithm of the Week⫸ Introducing Veo and Imagen 3 on Vertex A: This blog highlights Google Cloud's transformative generative AI tools, Veo and Imagen 3, on Vertex AI, enabling businesses to create high-quality videos and images effortlessly, reduce production costs, and unlock creative potential while ensuring safety and responsibility.⫸ MarS: A unified financial market simulation engine in the era of generative foundation models: Microsoft Research is advancing financial market analysis with MarS, a simulation engine powered by generative foundation models. By leveraging domain-specific financial data, MarS enables enhanced efficiency, insights, and adaptability for tasks like market prediction, risk assessment, and trading strategies.⫸ Advances in run-time strategies for next-generation foundation models: This blog explores advancements in frontier language models, highlighting OpenAI’s o1-preview achieving 96% accuracy on MedQA, outperforming GPT-4 with Medprompt. It examines run-time strategies, cost-efficiency, and prompting techniques for improving performance in medical challenge benchmarks.⫸ Microsoft Released MatterSimV1-1M and MatterSimV1-5M on GitHub: Microsoft's MatterSimV1-1M and MatterSimV1-5M, now on GitHub, revolutionize materials science with deep-learning models for precise, rapid simulations across diverse conditions. These tools predict properties like phase stability and Gibbs free energy, accelerating material discovery and engineering.⫸ Meet MegaParse: An Open-Source AI Tool for Parsing Various Types of Documents for LLM Ingestion. MegaParse is an open-source tool streamlining document preparation for large language models (LLMs). It supports diverse formats like PDFs, Word, and Excel, retaining data integrity while automating conversion into LLM-ready formats for efficient and accurate AI-driven workflows.⫸ Stacklock Releases Promptwright: A Python Library for Synthetic Dataset Generation Using an LLM (Local or Hosted). Promptwright, Stacklock's new Python library, simplifies synthetic dataset generation using local or hosted LLMs like OpenAI, Anthropic, and Gemini. It empowers developers with customizable prompts, multi-provider support, and seamless Hugging Face integration, bridging data gaps efficiently for AI projects.⫸ Amazon Introduces Amazon Nova: A New Generation of SOTA Foundation Models that Deliver Frontier Intelligence and Industry Leading Price-Performance. Amazon Nova redefines foundation models with versatile, cost-effective AI solutions via Amazon Bedrock. From text-only Micro to multimodal Pro, it balances scalability, affordability, and performance, offering extended context handling, fine-tuning, and robust global accessibility for diverse business needs.🚀 Trendspotting: What's Next in Tech Trends⫸ Use Gemini to optimize restaurant operations through AI visual analysis: Gemini 1.5 Pro revolutionizes business operations with multimodal AI and long-context window capabilities. From inventory management to safety assessments, it enables efficient AI-powered insights such as real-time kitchen analysis for restaurants, boosting productivity, training, and workplace safety.⫸ Integrating Machine Learning into Existing Software Systems: This blog explores key concepts, tools, and strategies for integrating machine learning models into existing software systems, addressing challenges like scalability, compatibility, and cost, while highlighting frameworks, containerization tools, MLOps platforms, and cloud solutions for seamless implementation.⫸ Enter The Void: An Open Source AI Coding IDE. This blog introduces Void, an open-source AI-powered code editor positioned as a community-driven alternative to Cursor. It highlights Void's features, customization capabilities, and steps for building the IDE locally, empowering developers to create and innovate independently.⫸ 10 GitHub Repositories to Master Reinforcement Learning: This blog highlights 10 GitHub repositories to master reinforcement learning, offering free resources, including tutorials, projects, and algorithms. It’s a practical guide for learners to explore RL concepts, apply them through projects, and stay updated on the latest trends.⫸ Tips for Handling Large Datasets in Python: This blog provides practical tips and tools for handling large datasets in Python, including memory-efficient techniques, parallel and distributed computing with Dask and PySpark, and chunked processing with Pandas to streamline big data workflows.⫸ How to Implement Cross-Lingual Transfer Learning with mBERT in Hugging Face Transformers? This article explains how to fine-tune the multilingual BERT (mBERT) model from Hugging Face for cross-lingual transfer learning, showcasing its ability to generalize across languages by training on English data and evaluating on French datasets.⫸ Simplify data access for your enterprise using Amazon SageMaker Lakehouse: This article explains how to use Amazon SageMaker Lakehouse to unify data from warehouses and lakes, enabling secure, scalable analytics and machine learning for businesses. It showcases a case study on customer churn prediction and provides a step-by-step implementation guide.🛠️ Platform Showdown: Comparing ML Tools & Services⫸ Fireworks.ai: Lighting up gen AI through a more efficient inference engine: This blog introduces Fireworks AI, an advanced gen AI inference engine designed to help enterprises scale, optimize costs, and deploy AI models efficiently. It highlights Fireworks’ collaboration with Google Cloud and NVIDIA to deliver cutting-edge, scalable, and secure AI solutions.⫸ Simplify Mainframe Modernization using Amazon Q Developer generative AI Agents: This blog introduces Amazon Q Developer, a generative AI-powered solution for mainframe modernization. It automates code analysis, planning, and refactoring, enabling faster, cost-effective transitions to cloud-native architectures while preserving critical application logic and improving agility, security, and scalability.⫸ How to Interpret Matrix Expressions—Transformations? This article is the first in a series designed to simplify matrix algebra for data scientists. It focuses on interpreting complex matrix expressions, providing intuitive, practical explanations of key concepts like transformations, transposition, and inverses, with a focus on machine learning applications.⫸ Introducing Univariate Exemplar Recommenders: how to profile Customer Behavior in a single vector: This blog explores exemplar recommenders, a vector-based architecture for recommendation systems that enhances scalability and accuracy. It introduces multivariate and univariate approaches, highlights clustering methods, and focuses on improving recommendation variance while addressing computational challenges in user preference profiling.⫸ SQL vs. Calculators: Building Champion/Challenger Tests from Scratch. This blog explores the transformative power of champion-challenger testing (A/B testing) in business decision-making, using SQL for implementation. It discusses the $300 million button case, test setup, key metrics, and sample size calculations to optimize strategies and drive measurable results.⫸ Training Language Models on Google Colab: This blog provides a guide to fine-tuning large language models on Google Colab efficiently. It addresses Colab's limitations by utilizing Google Drive for saving checkpoints, enabling resumption of interrupted training, and offers reusable code for persistent experimentation across sessions.⫸ PostgreSQL: Query Optimization for Mere Humans. This blog explores how to optimize SQL queries by leveraging PostgreSQL's EXPLAIN and EXPLAIN ANALYZE clauses. It demystifies execution plans, identifying bottlenecks, and improving database performance with practical tips and a deep dive into execution plan anatomy.📊 Success Stories: Real-World ML Case Studies⫸ Becoming a Data Scientist: What I Wish I Knew Before Starting. This blog outlines a practical roadmap for aspiring data scientists, emphasizing foundational skills in mathematics, programming, SQL, and machine learning. It stresses business impact, focusing on the Pareto Principle, and encourages hands-on experience to transition effectively into the data science field.⫸ From Retrieval to Intelligence: Exploring RAG, Agent+RAG, and Evaluation with TruLens. This blog explores enhancing Large Language Models using Retrieval Augmented Generation (RAG) with LlamaIndex, addressing limitations in detail specificity and outdated knowledge, while integrating TruLens for performance metrics and emphasizing efficient, expert-like responses over extensive web searches.⫸ How to Build Prompt Engineering Expertise at Your Company? This post explores whether companies should hire dedicated prompt engineers or grow this expertise internally, highlighting the role’s evolving nature, necessary skills like creativity and curiosity, and strategies for nurturing prompt engineering talent to leverage generative AI effectively.⫸ Machine Learning Experiments Done Right: This post outlines a detailed checklist for conducting rigorous, reproducible machine learning experiments, addressing design, data selection, systematic testing, and cross-validation to ensure valid and reliable results, while avoiding common pitfalls like data contamination and misreporting.⫸ Model Validation Techniques: This post explains 12 model validation techniques for testing machine learning model reliability, showcasing their evolution and distinctions through a consistent dataset example, focusing on practical applications and why choosing the right method matters.⫸ Making News Recommendations Explainable with Large Language Models: This post explores the use of Large Language Models (LLMs) for news article recommendation at DER SPIEGEL, highlighting their predictive accuracy, explainability, and potential to enhance user engagement. Challenges include high costs, slow processing, and optimization opportunities for improved scalability.⫸ Why Internal Company Chatbots Fail and How to Use Generative AI in Enterprise with Impact? This article highlights a process-driven approach to generative AI in enterprises, emphasizing AI process orchestration over chatbots. It discusses designing structured workflows with reusable templates to improve reproducibility, efficiency, and quality, avoiding over-reliance on inconsistent chatbot interactions.We’ve got more great things coming your way—see you soon!*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}

Neural Magic’s Sparse Llama 3.1 8B, LangChain’s Document Retriever, LLMs Meet Knowledge GraphsLearn the Roadmap to making $100k using LinkedIn & AI (for free) 🚀This AI-powered workshop is designed for experienced professionals and self-employed individuals ready to scale their careers or businesses.In just 90 minutes, you’ll learn how to:👉 Automate lead generation to grow your business effortlessly.👉 Master LinkedIn's $100K strategy to increase revenue while saving time.👉 Use AI to secure high-paying roles, bypassing endless applications.Join Vaibhav Sisinty, a LinkedIn influencer with over 400K followers, who’s transformed the LinkedIn strategies of over 200,000 professionals. Normally valued at $399, this workshop is free for the first 100 readers.Claim Your Free Spot Now (Only 100 seats available!)Sponsored🗞️Welcome to DataPro #122 – Your Weekly DS& ML Spark! 🌟Stay in the loop with this week’s top discoveries in AI, ML, and data science! From breakthrough tools to actionable insights, we’ve got everything you need to sharpen your edge and supercharge your projects. Let’s dive in!🔍Spotlight: This Week’s Star Models✦ Create Smarter Chatbots:Build a self-escalating conversational agent using Webhooks and Generators.✦ Foundry Unleashed:An AI startup redefining agent-building and evaluation.✦ StereoAnything:The AI powerhouse for robust stereo matching solutions.✦ SmolVLM by Hugging Face:A 2B parameter model for on-device vision-language tasks.✦ FastDraft by Intel AI:Affordable pre-training to align models for speculative decoding.✦ Neural Magic’s Sparse Llama 3.1 8B:Efficient inference with smaller, high-performing models.🚀Trendspotting: What's Hot in AI✦ LLMs Meet Knowledge Graphs:A cutting-edge method to search enterprise data assets.✦ Whisper-NER by aiOla:Open-source transcription meets entity recognition.✦ Fugatto by NVIDIA AI:Transforming text and audio into music, voice, and sound.✦ FunctionChat-Bench:Testing LLMs’ function-calling chops in real-world scenarios.✦ Apple AIMv2:The next-gen open-set vision encoders are here!🛠️Tool Talk: Platforms in Action✦ Taming LLM Hallucinations:Intervene like a pro with Amazon Bedrock Agents.✦ Arch 0.1.3:The open-source proxy for intelligent AI agent management.✦ AgentAuth by Composio:The ultimate authentication solution for AI agents.✦ AI2’s OLMo 2:Open-source LMs trained on a whopping 5T tokens.✦ Mistral on Vertex AI:Large-instruct models pushing the boundaries.✦ Gen AI for DevOps:Turbocharge continuous delivery pipelines.📊In Action: Real-World Wins✦ Cyber Defense with LLMs:Sophos shares strategies using Amazon’s tools.✦ Smarter Transformers:Tips for optimizing models for variable-length inputs.✦ Explainable AI Pipelines:Build with MLflow for better transparency.✦ DIY Personal Assistants:Use agents and tools to create your own.✦ LangChain’s Document Retriever:A second look at enhancing retrieval accuracy.🌍Buzz Corner: What’s Trending Now✦ DIY AI Projects:Budget-friendly app-building ideas for everyone.✦ Coding with Cursor:Pro tips to boost efficiency 10x.✦ Redis 101:A beginner’s guide to setup and installation.✦ Python for DS Apps:Build a data science app in just 10 steps.✦ Mistral 7B Simplified:Insights into efficient language modeling.Enjoy exploring, learning, and building this week!Stay tuned and stay inspired – there’s always something new to discover in the ever-evolving world of Data Science and Machine Learning!Take our weekly survey and get a free PDF copy of our best-selling book,"Interactive Data Visualization with Python - Second Edition."We appreciate your input and hope you enjoy the book!Share Your Insights and Shine! 🌟💬Cheers,Merlyn Shelley,Editor-in-Chief, Packt.📚 Packt Signature Series: Must-Reads & Author Insights➽ RAG-Driven Generative AI: This new title, RAG-Driven Generative AI, is perfect for engineers and database developers looking to build AI systems that give accurate, reliable answers by connecting responses to their source documents. It helps you reduce hallucinations, balance cost and performance, and improve accuracy using real-time feedback and tools like Pinecone and Deep Lake. By the end, you’ll know how to design AI that makes smart decisions based on real-world data—perfect for scaling projects and staying competitive! Start your free trial for access, renewing at $19.99/month.eBook $24.99 $35.99Print + eBook $43.99➽ Building Production-Grade Web Applications with Supabase: This new book is all about helping you master Supabase and Next.js to build scalable, secure web apps. It’s perfect for solving tech challenges like real-time data handling, file storage, and enhancing app security. You'll even learn how to automate tasks and work with multi-tenant systems, making your projects more efficient. By the end, you'll be a Supabase pro! Start your free trial for access, renewing at $19.99/month.eBook $15.99 $31.99Print + eBook $39.99➽ Python Data Cleaning and Preparation Best Practices: This new book is a great guide for improving data quality and handling. It helps solve common tech issues like messy, incomplete data and missing out on insights from unstructured data. You’ll learn how to clean, validate, and transform both structured and unstructured data—think text, images, and audio—making your data pipelines reliable and your results more meaningful. Perfect for sharpening your data skills! Start your free trial for access, renewing at $19.99/month.eBook $24.99 $35.99Print + eBook $44.99🔍 Model Breakdown: Unveiling the Algorithm of the Week➽ Create a self-escalating chatbot in Conversational Agents using Webhook and Generators: This blog outlines how data professionals can design a self-escalating chatbot using Google Cloud tools like Vertex AI and Dialogflow CX. It focuses on optimizing user interactions, streamlining workflows, leveraging data for continuous learning, and ensuring scalable AI solutions.➽ Meet Foundry: An AI Startup that Builds, Evaluates, and Improves AI Agents. This blog explores Foundry, a Y Combinator-backed platform revolutionizing AI agent development and management. Designed for data professionals, it simplifies deployment, enhances transparency, integrates effortlessly with existing systems, and empowers organizations to scale automation with reliability and efficiency.➽ StereoAnything: A Highly Practical AI Solution for Robust Stereo Matching. If you’re working on stereo matching,StereoAnythingis a game-changer. It tackles the toughest challenges in depth estimation and 3D scene understanding with smarter training methods and diverse datasets. Perfect for projects in robotics, self-driving cars, or AR—give it a look!➽ Hugging Face Releases SmolVLM: A 2B Parameter Vision-Language Model for On-Device Inference. SmolVLM is a lightweight vision-language model designed for on-device use, delivering fast, efficient performance without requiring expensive hardware. Ideal for laptops and consumer GPUs, it balances speed and accuracy, making advanced AI tasks accessible to researchers, developers, and hobbyists.➽ Intel AI Research Releases FastDraft: A Cost-Effective Method for Pre-Training and Aligning Draft Models with Any LLM for Speculative Decoding. FastDraft accelerates LLM inference by aligning efficient draft models with target LLMs, improving acceptance rates, reducing memory demands, and enabling faster processing. Perfect for resource-constrained tasks, it offers up to 3x speedup in real-world applications.➽ Neural Magic Releases 2:4 Sparse Llama 3.1 8B: Smaller Models for Efficient GPU Inference. Sparse Llama 3.1 8B redefines efficiency in AI with 50% pruning, reduced latency, and GPU compatibility. It balances strong performance with sustainability, making advanced AI accessible to more users while cutting costs and lowering its environmental impact.🚀 Trendspotting: What's Next in Tech Trends➽ Search enterprise data assets using LLMs backed by knowledge graphs: Struggling to find your enterprise data? This blog introduces a generative AI-powered semantic search solution that combines large language models with knowledge graphs, letting you search across complex data sources effortlessly using natural language for precise, contextual results.➽ aiOla Releases Whisper-NER: An Open Source AI Model for Joint Speech Transcription and Entity Recognition. Ever wondered why speech recognition struggles with understanding names or specialized terms? EnterWhisper-NER, aiOla's open-source model that transcribes speech while recognizing entities in real time, offering contextual accuracy, context, and privacy for industries like healthcare and legal services.➽ NVIDIA AI Unveils Fugatto: A 2.5 Billion Parameter Audio Model that Generates Music, Voice, and Sound from Text and Audio Input. How can AI truly revolutionize music and audio production? NVIDIA’sFugattoanswers this by combining text and audio prompts to create, transform, and manipulate sounds. With versatile capabilities like ComposableART, it empowers artists to redefine creative boundaries effortlessly.➽ FunctionChat-Bench: Comprehensive Evaluation of Language Models' Function Calling Capabilities Across Interactive Scenarios. What if AI could handle complex tool interactions while chatting like a human?FunctionChat-Benchsets a new standard, testing language models’ ability to call functions fluidly in dynamic, multi-turn conversations, reshaping how AI integrates with tools and users.➽ Apple Releases AIMv2: A Family of State-of-the-Art Open-Set Vision Encoders: Ever wished for a vision model that could handle images and text effortlessly, no matter the task? AIMv2 delivers exactly that by combining scalability, autoregressive decoding, and versatility to tackle real-world multimodal challenges with precision.🛠️ Platform Showdown: Comparing ML Tools & Services➽ Reducing hallucinations in large language models with custom intervention using Amazon Bedrock Agents: Can AI effectively tackle hallucinations in real time? Using Amazon Bedrock Agents, this blog showcases a RAG-powered chatbot achieving up to 20% improvement in answer relevancy, dynamically managing hallucinations with customized workflows and reducing development costs by streamlining interventions.➽ Meet Arch 0.1.3: Open-Source Intelligent Proxy for AI Agents. Optimize AI agent communication withArch 0.1.3, an intelligent proxy built on Envoy. By reducing latency by 30% and enabling dynamic routing and real-time monitoring, it ensures secure, efficient, and scalable workflows for modern AI-powered environments.➽ Composio Introduces AgentAuth: The Comprehensive Auth Solution Designed for AI Agents. Streamline authentication for AI agents withAgentAuthby Composio. Simplify connections to over 250 apps, reduce authentication management time by 60%, and enhance security across frameworks like LangChainAI and llama_index, enabling seamless integration for advanced AI workflows.➽ The Allen Institute for AI (AI2) Releases OLMo 2: A New Family ofOpen-Sourced 7Band13BLanguage Models Trained on up to5TTokens. Advance your AI projects withOLMo 2, the Allen Institute’s open-source language models. Trained on 5 trillion tokens, OLMo 2 delivers up to 13B parameters, outperforming proprietary models like Llama-3.1, setting new benchmarks in accessibility, stability, and performance.➽ Mistral AI’s Large-Instruct-2411 on Vertex AI: The new Mistral-Large-Instruct-2411 is now available on Vertex AI, offering advanced capabilities with 123B parameters. This model is tailored for complex agentic workflows, retrieval-augmented generation (RAG), and code generation tasks. It provides straightforward deployment options, allowing you to customize it with your unique data and requirements. With enterprise-grade security and a fully managed infrastructure, Mistral-Large-Instruct-2411 enhances AI integration while maintaining flexibility and scalability for your business needs.➽ Boost your Continuous Delivery pipeline with Generative AI: What if your CI/CD pipeline could do more than just automate builds? By integrating Gemini models in Vertex AI, you can enhance code reviews, generate detailed release notes, and streamline software delivery while maintaining high-quality development standards.📊 Success Stories: Real-World ML Case Studies➽ Using LLMs to fortify cyber defenses: Sophos’s insight on strategies for using LLMs with Amazon Bedrock and Amazon SageMaker: What if AI could revolutionize security operations? SophosAI leverages Anthropic’s Claude 3 Sonnet on Amazon Bedrock to simplify SOC tasks, achieving 88% SQL query accuracy, prioritizing incident severity, and summarizing alerts, making cybersecurity operations faster and more efficient.➽ Optimizing Transformer Models for Variable-Length Input Sequences: Can generative AI models handle variable-length inputs more efficiently? This blog dives into optimizing attention mechanisms like FlashAttention2 to reduce padding overhead, improve runtime performance, and cut costs for Transformer-based systems in real-world applications.➽ Explainable Generic ML Pipeline with MLflow: Why struggle with switching ML frameworks? This blog builds on a beginner-friendly guide to usingMLflow.pyfuncfor algorithm-agnostic pipelines, demonstrating advanced features like pre-processing, handling missing data, and model explainability for seamless deployment and scalability.➽ Build your Personal Assistant with Agents and Tools: Do you settle for chatbots that can’t go beyond static responses? This blog shows how to enhance LLMs with tools, agents, and chains, enabling them to interact with real-time data, automate workflows, and solve complex tasks dynamically.➽ LangChain’s Parent Document Retriever — Revisited: Ever wondered how LLMs can generate better, context-rich answers? This blog dives into retrieval-augmented generation (RAG) and techniques like Parent Document Retrieval to enhance performance, provide broader context, and make AI outputs more accurate and reliable.🌍 ML Newsflash: Latest Industry Buzz & Discoveries➽ DIY AI: Building Your AI Apps on a Shoestring Budget. This post explains how to build a basic AI-powered application using pre-trained models like GPT-4. It covers differences between AI and non-AI apps, showcases AI use cases like NLP and computer vision, and provides a step-by-step tutorial for beginners.➽ Effectively Using Cursor for 10x Coding: Can an AI-powered IDE change the way you code? This post exploresCursor, packed with features like code autocompletion, interactive chat, and smart editing, designed to elevate your coding workflow and amplify productivity like never before.➽ Getting Started with Redis: Installation and Setup Guide. Are you curious about setting up Redis quickly for your next project?This guide walks you through installing and configuring Redis on Linux, Windows, and macOS, ensuring you’re ready to leverage its speed and scalability.➽ Build a Data Science App with Python in 10 Easy Steps: This blog offers a step-by-step tutorial on building a simple data science app. Using Python, scikit-learn, and FastAPI, it demonstrates data preprocessing, model training, and creating an API for serving predictions, using scikit-learn’s wine dataset.➽ Mistral 7B Explained: Towards More Efficient Language Models. This blog explores the innovations behindMistral 7B, a smaller yet highly efficient large language model. It delves into its architecture, efficient components like Sliding Window Attention, and how it balances performance with fewer parameters, making it a significant advancement in AI.We’ve got more great things coming your way—see you soon!*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}

Lingma SWE-GPT, BiomedParse, Research Assistants for Docs, Email Automation with Amazon BedrockScale your scrapers with Apify’s Black Friday Boost planGet a 30% prepaid usage bonus on Apify this Black Friday.Scrape data for LLMs, machine learning, competitive intelligence, product mapping, or any AI use cases.Use ready-made scrapers or build your own.The Boost plan ends December 5 - grab it before it’s gone!Claim Your Bonus Now!Sponsored🗞️Welcome to DataPro #121 – Your Weekly DS & ML Highlights! 🌟Stay on top of the ever-evolving world of Data Science, AI, and ML! This week, we’ve curated the hottest resources, tools, and breakthroughs to empower your projects and sharpen your skills. Let’s explore!🔍 Top Picks: Must-Know Insights for Data Pros◘ Cortex for Local LLMs: Simplify running local language models.◘ AnythingLLM: Your all-in-one LLM app.◘ Smarter Maps with GPT-4o: Explore fine-tuning for advanced geospatial tools.◘ AI for Good: Tackling real-world challenges with Yasuyuki Matsushita at Microsoft Research Asia.◘ BiomedParse: Revolutionize biomedical image analysis with this foundation model.◘ Orca-AgentInstruct: Harness synthetic data through agentic flows.◘ GraphRAG: Boost global search with dynamic community selection.🚀 Next-Level Tech Trends◘ Google Cloud’s Translation AI Updates: Breaking boundaries in translation technology.◘ Caravan MultiMet by Google AI: Exploring multi-model alignment.◘ Infinite-Length Video Generation: Dive into "Meet The Matrix."◘ FluidML: Smarter runtime management for ML inference.◘ AWS Multi-Agent Orchestrator: Seamlessly manage AI agents.◘ DeepSeek’s Reasoning Engine: Unveiling DeepSeek-R1-Lite-Preview.◘ Pixtral Large: Mistral AI’s 124B multimodal innovation.◘ XiYan-SQL by Alibaba Research: The ultimate Text-to-SQL framework.◘ Lingma SWE-GPT: Open-source solutions for software development challenges.🛠️ ML Tools & Tactics◘ AI-Powered Prompt Writing: Save time with smarter designs.◘ NER with Hugging Face: A simple guide to Named Entity Recognition.◘ 10 Python Libraries: Essential tools for data analysts.◘ ETL Pipelines: Develop robust workflows for data projects.◘ Advanced SQL Techniques: Master data manipulation like a pro.◘ Python + DuckDB: Speed up your data analysis.◘ Google Cloud Data Security: A guide to building a secure platform.📊 In Action: Real-World ML Wins◘ Why AI Strategies Fail: Common pitfalls and how to avoid them.◘ Data-Driven Customer Systems: Build better management frameworks.◘ Research Assistants for Docs: Automate document creation with AI.◘ Feature Engineering in Healthcare: Transform insights with smart techniques.◘ 3D Imaging with Nvidia LLaMa-Mesh: Bring your visuals to life.◘ Multimodal Models: LLMs that see and hear.◘ Understanding Data Labeling: A hands-on guide.◘ Cost Savings with Ray on Amazon EKS: How Vannevar Labs cut ML costs by 45%.🌍 Industry Buzz & Discoveries◘ Optimizing Transformers: Make attention layers work harder.◘ Neural Network Quantization: Tips to streamline your models.◘ Email Automation with Amazon Bedrock: Smarter Q&A workflows.◘ Integrated Text & Image Classification: Next-gen data analysis.◘ NetworkX in Python: Master graphs and networks with ease.◘ Fixing Cross-Validation Visuals: Avoid common pitfalls in data visualization.Stay tuned and stay inspired – there’s always something new to discover in the ever-evolving world of Data Science and Machine Learning!Take our weekly survey and get a free PDF copy of our best-selling book,"Interactive Data Visualization with Python - Second Edition."We appreciate your input and hope you enjoy the book!Share Your Insights and Shine! 🌟💬Cheers,Merlyn Shelley,Editor-in-Chief, Packt.📚 Packt Signature Series: Must-Reads & Author Insights➽ RAG-Driven Generative AI: This new title, RAG-Driven Generative AI, is perfect for engineers and database developers looking to build AI systems that give accurate, reliable answers by connecting responses to their source documents. It helps you reduce hallucinations, balance cost and performance, and improve accuracy using real-time feedback and tools like Pinecone and Deep Lake. By the end, you’ll know how to design AI that makes smart decisions based on real-world data—perfect for scaling projects and staying competitive! Start your free trial for access, renewing at $19.99/month.eBook $24.99 $35.99Print + eBook $43.99➽ Building Production-Grade Web Applications with Supabase: This new book is all about helping you master Supabase and Next.js to build scalable, secure web apps. It’s perfect for solving tech challenges like real-time data handling, file storage, and enhancing app security. You'll even learn how to automate tasks and work with multi-tenant systems, making your projects more efficient. By the end, you'll be a Supabase pro! Start your free trial for access, renewing at $19.99/month.eBook $15.99 $31.99Print + eBook $39.99➽ Python Data Cleaning and Preparation Best Practices: This new book is a great guide for improving data quality and handling. It helps solve common tech issues like messy, incomplete data and missing out on insights from unstructured data. You’ll learn how to clean, validate, and transform both structured and unstructured data—think text, images, and audio—making your data pipelines reliable and your results more meaningful. Perfect for sharpening your data skills! Start your free trial for access, renewing at $19.99/month.eBook $24.99 $35.99Print + eBook $44.99🔍 Model Breakdown: Unveiling the Algorithm of the Week⫸ Run Local LLMs with Cortex: This blog introduces Cortex, a tool that allows you to run and customize local LLMs easily on your machine. It guides you through installation, model selection, and usage, making AI accessible even with standard hardware.⫸ AnythingLLM: The LLM Application You’ve Been Waiting For. This blog introduces AnythingLLM, an open-source platform that helps you build private ChatGPT-like agents. It offers advanced capabilities, privacy, and flexibility, with step-by-step instructions on getting started for various use cases.⫸ Building smarter maps with GPT-4o vision fine-tuning: This blog highlights Grab's innovative use of GPT-4o vision fine-tuning to improve its mapping service, GrabMaps. By enhancing localization and automation in mapmaking, Grab reduces costs, increases accuracy, and boosts data trust for Southeast Asia's dynamic landscape.⫸ Tackling societal challenges with AI at Microsoft Research Asia - Tokyo. This blog celebrates the opening of Microsoft Research’s new Tokyo lab, focusing on embodied AI, societal challenges, and industry innovation. Led by Yasuyuki Matsushita, it aims to drive local and global AI advancements through collaboration and talent development.⫸ BiomedParse: A foundation model for smarter, all-in-one biomedical image analysis. This blog introduces BiomedParse, an advanced framework for holistic biomedical image analysis. It unifies object recognition, detection, and segmentation into a single model, offering faster, more accurate insights by using natural-language prompts for medical image analysis.⫸ Orca-AgentInstruct: Agentic flows can be effective synthetic-data generators. This blog introduces Orca-AgentInstruct, an agentic framework for generating diverse, high-quality synthetic data to fine-tune language models. By leveraging agentic flows, it enables scalable, autonomous data generation, leading to substantial performance improvements across multiple benchmarks.⫸ GraphRAG: Improving global search via dynamic community selection. This blog introduces GraphRAG, an advanced method for handling "global" queries using a dynamic global search. It efficiently utilizes a hierarchical knowledge graph, reducing costs by pruning irrelevant community reports and improving response quality for abstract questions.🚀 Trendspotting: What's Next in Tech Trends⫸ Sharing the latest updates to Google Cloud’s Translation AI: This blog introduces Google Cloud's Translation AI in Vertex AI, offering advanced tools for accurate, customizable translation. It includes two options: Translation API Basic for speed and consistency, and Translation API Advanced for tailored, high-quality translations at scale.⫸ Google AI Research Introduces Caravan MultiMet: This blog introduces the Caravan MultiMet extension, a breakthrough in large-sample hydrology. By integrating real-time forecast and nowcast data into the Caravan dataset, it enhances hydrological model accuracy, improving forecasting, benchmarking, and water resource management.⫸ Meet The Matrix: A New AI Approach to Infinite-Length and Real-Time Video Generation. This blog introduces The Matrix, a groundbreaking world model for generating infinite-length, real-time video simulations with high fidelity. It uses advanced diffusion techniques to enable interactive, scalable simulations across both game and real-world environments, revolutionizing video generation for gaming, training, and VR.⫸ Meet FluidML: A Generic Runtime Memory Management and Optimization Framework for Faster, Smarter Machine Learning Inference. This blog introduces FluidML, an advanced framework designed to optimize machine learning inference on edge devices. By improving memory layout, graph segmentation, and scheduling, FluidML achieves significant reductions in latency and memory usage, enabling real-time deployment of complex models in resource-constrained environments.⫸ AWS Releases 'Multi-Agent Orchestrator': A New AI Framework for Managing AI Agents and Handling Complex Conversations. This blog introduces AWS's Multi-Agent Orchestrator, a framework designed to manage multiple AI agents. It intelligently routes queries, maintains context, and supports flexible deployment across various environments, enhancing the scalability and coherence of conversational AI systems.⫸ DeepSeek Introduces DeepSeek-R1-Lite-Preview with Complete Reasoning Outputs Matching OpenAI o1. This blog introduces DeepSeek-R1-Lite-Preview, a model designed to enhance transparency in AI reasoning. By incorporating Chain-of-Thought capabilities, it provides step-by-step explanations for complex tasks, improving trust and understanding in AI-driven problem-solving.⫸ Mistral AI Releases Pixtral Large: A 124B Open-Weights Multimodal Model Built on Top of Mistral Large 2. This blog introduces Pixtral Large, a 124 billion-parameter multimodal AI model by Mistral AI. Built on Mistral Large 2, it integrates text, images, and other data types, offering open weights for customizable research and application development.⫸ Alibaba Research Introduces XiYan-SQL: A Multi-Generator Ensemble AI Framework for Text-to-SQL. This blog introduces XiYan-SQL, an innovative NL2SQL framework that enhances query generation through multi-generator ensemble strategies and advanced schema representation. With superior performance across multiple benchmarks, it balances accuracy, adaptability, and diversity for complex database interactions.⫸ Lingma SWE-GPT: Pioneering AI-Assisted Solutions for Software Development Challenges with Innovative Open-Source Models. This blog introduces Lingma SWE-GPT, an open-source LLM series designed for software engineering tasks. With improved fault localization, patch generation, and iterative reasoning, it bridges performance gaps between open and closed-source models while remaining cost-effective and scalable.🛠️ Platform Showdown: Comparing ML Tools & Services⫸ Save time on prompt design with AI-powered prompt writing: This blog introduces new features in Vertex AI to simplify prompt engineering: "Generate prompt" for quickly creating effective prompts based on objectives, and "Refine prompt" for improving them with AI-driven suggestions, streamlining the workflow and enhancing prompt quality.⫸ How to Implement Named Entity Recognition with Hugging Face Transformers? This blog demonstrates how to perform Named Entity Recognition (NER) using Hugging Face’s Transformers library. By using a pre-trained BERT model fine-tuned for NER tasks, the tutorial walks through tokenization, entity identification, and results interpretation, helping developers extract valuable insights from text.⫸ 10 Python Libraries Every Data Analyst Should Know: This blog highlights essential Python libraries for data analysts. It covers tools for data retrieval (Requests, Beautiful Soup), manipulation (NumPy, Pandas, Polars), statistical analysis (Statsmodels, SciPy), and visualization (Seaborn), along with database interaction (SQLAlchemy), all aimed at simplifying and enhancing the data analysis workflow.⫸ Developing Robust ETL Pipelines for Data Science Projects: This blog introduces the process of building an ETL pipeline for data science projects. It covers the steps of Extracting, Transforming, and Loading data, using Python libraries like Pandas and SQLite to automate the data cleaning and storage process for efficient analysis.⫸ 7 Advanced SQL Techniques for Data Manipulation in Data Science: This blog highlights seven advanced SQL techniques for data manipulation in data science. These techniques include subqueries, correlated subqueries, Common Table Expressions (CTEs), and recursive queries, all of which help streamline complex queries, restructure data, and handle hierarchical data efficiently.⫸ A Guide to Data Analysis in Python with DuckDB: This blog introduces DuckDB, an in-process OLAP database for analyzing data in Python. It demonstrates how to set up the environment, install DuckDB, and query data from CSV files using SQL, making data analysis with pandas and other data sources more efficient.⫸ Learn how to build a secure data platform with Google Cloud ebook: This blog explores how Google Cloud's data security tools can protect your business data while fostering innovation. It covers encryption, access controls, compliance, and monitoring to help safeguard your data in today’s complex security landscape.📊 Success Stories: Real-World ML Case Studies⫸ The Root Cause of Why Organizations Fail With Data & AI: This article explains why many companies struggle to monetize their data and how the lack of a clear business strategy is the root cause. It emphasizes the importance of aligning business strategies with data initiatives for success.⫸ How to Build a Data-Driven Customer Management System: This article explores how customer base management (CBM) systems help businesses optimize pricing, predict churn, and enhance decision-making. It covers foundational components like ELT, churn modeling, and dashboards, and examines how advanced features can provide a strategic edge.⫸ Building a Research Assistant That Can Write to Google Docs: This article, part two of a series, explains how to connect a research agent to Google Docs using LangGraph and Tavily. It covers setting up Google Drive and Docs APIs, creating folders, and uploading documents programmatically.⫸ Feature Engineering Techniques for Healthcare Data Analysis: This article continues a feature engineering project focused on healthcare data, specifically on handling patient diagnosis data to uncover hidden insights. It highlights the importance of domain knowledge in transforming raw data, using techniques like comorbidity analysis to create meaningful features for better predictions and outcomes.⫸ Generate 3D Images with Nvidia’s LLaMa-Mesh: This article explores NVIDIA's LLaMA-Mesh, a model that generates 3D mesh objects from natural language descriptions. It highlights how vertex quantization and OBJ format enable seamless 3D object creation and understanding, with applications across various industries.⫸ Multimodal Models — LLMs That Can See and Hear: This article introduces multimodal AI, focusing on models that combine text and image processing. It explores using LLaMA 3.2 Vision for image-to-text tasks like visual question answering, demonstrating the power of LLMs in handling multiple modalities.⫸ Understanding Data Labeling (Guide): This article explains the importance of data labeling in machine learning, discussing its role in supervised learning, types of labeling (e.g., image classification, sentiment analysis), and various approaches, including human-in-the-loop and automated methods.⫸ How Vannevar Labs cut ML inference costs by 45% using Ray on Amazon EKS? This post details how Vannevar Labs optimized its ML inference workloads using Ray, Karpenter, and Amazon EKS, achieving a 45% reduction in costs. They employed Ray Serve for efficient inference, used Karpenter for optimized instance selection, and leveraged fractional GPUs for improved resource utilization.🌍 ML Newsflash: Latest Industry Buzz & Discoveries⫸ Increasing Transformer Model Efficiency Through Attention Layer Optimization: This article explores optimization techniques for attention layers in Transformer models using PyTorch. It covers various methods like PyTorch SDPA, FlashAttention, and third-party solutions such as Transformer Engine to enhance computational efficiency and reduce resource consumption, offering insights into real-world performance improvements.⫸ Quantizing Neural Network Models: This post discusses techniques for quantizing AI models to reduce their size and computational cost while maintaining accuracy. It focuses on two methods: Post-Training Quantization (PTQ) and Quantization Aware Training (QAT), highlighting their advantages, challenges, and use cases.⫸ Automate Q&A email responses with Amazon Bedrock Knowledge Bases: This post discusses automating email responses using generative AI, combining Retrieval Augmented Generation (RAG) and Amazon Bedrock Knowledge Bases. It outlines a solution that improves HR operations by automating email replies with accurate, contextually relevant information from company knowledge bases.⫸ Integrating Text and Images for Smarter Data Classification: This post provides a technical guide on building a multimodal AI pipeline for classifying mixed text and image data. Using Gemini 1.5 and LangChain, the tutorial covers setting up the system for image-text classification, including key steps like defining output schemas, encoding image data, and handling structured outputs for accurate classification.⫸ Navigating Networks with NetworkX: A Short Guide to Graphs in Python. This post introduces NetworkX, a powerful library for building, analyzing, and visualizing graphs. It explains how to create graphs, add nodes and edges with attributes, and visualize them using Matplotlib. The post also demonstrates these concepts with examples, including the famous Zachary’s Karate Club network.⫸ Why Most Cross-Validation Visualizations Are Wrong (And How to Fix Them)? This post explores how current cross-validation diagrams often confuse learners and suggests a better approach. It discusses how traditional visualizations rely too much on color and movement, which mislead understanding, and offers a simpler, more intuitive design for explaining cross-validation processes.We’ve got more great things coming your way—see you soon!*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}

Sentence Transformers v3.3.0 by Hugging Face, Spotting Social Media Anomalies with AI, OpenFLAMEThe top ten nastiest vulnerabilities of Q3Are you exposed? Download the Q3 2024 Vulnerability Watch report to find out.The usual vulns from Microsoft and VMware make the list, but there are some surprises too. Chances are at least one of these vulnerabilities is lurking in your environment. The Watch report outlines the exposure risks and provides actionable steps to mitigate each included CVE, helping reduce your cyber risk. Download the report and stay one step ahead of the most-critical exposure risk.Download nowSponsored🗞️ Welcome to DataPro #120 – Your Weekly Data Science & ML Wizardry! 🌟Get your weekly dose of the freshest DS and ML updates designed to elevate your projects, refine models, and keep you in sync with the latest breakthroughs. From powerful resources to boost model accuracy to emerging trends and practical guides, this edition is packed with insights you won’t want to miss!🔍 Algorithm Spotlight: This Week’s Model Unpacked◘ Optimizing Retrieval in RAG Pipelines with Huggingface Transformers: Discover how reranking can enhance retrieval for RAG.◘ Vision Transformer with BatchNorm: A closer look at Vision Transformer architecture improvements.◘ Fixie AI's Ultravox v0.4.1 Release: Updates and capabilities of Fixie AI's new release.◘ FinSafeNet: Protecting Digital Banking with Deep Learning: From fraud detection to real-time security, see how deep learning is safeguarding finances.◘ Nous Research Debuts Forge Reasoning API Beta & Nous Chat: Explore new tools from Nous Research designed for advanced reasoning and interactive ML models.🚀 What’s Hot: The Next Big ML Trends◘ Pushing the Boundaries of Audio Generation – Google DeepMind: The latest advancements in synthetic audio.◘ Introducing ChatGPT Search: OpenAI integrates search into ChatGPT.◘ AI Text and Synthetic Protein Watermarking: The emerging field of watermarking AI outputs.◘ DeepSeek AI’s JanusFlow: A new framework for cohesive image understanding and generation.◘ TensorOpera AI’s Fox-1 Series: Lightweight models, including the new Fox-1-1.6B series, pushing SLM capabilities.◘ OpenAI’s January Release – Everyday AI Agents: AI agents are soon stepping into daily life automation.🛠️ Tool Talk: ML Platforms Compared◘ Master Data Cleaning in Python – 7 Strategies: Essential tips to refine your data cleaning prowess.◘ Combining Pandas with SQL for Data Analysis: How blending these tools can elevate your data skills.◘ 5 Free Learning Resources for LLM Agents: Perfect for upskilling in large language models.◘ Navigating AI Regulations – Innovation Meets Protection: A dive into balancing AI progress with ethical guardrails.◘ 7 Python Projects to Strengthen Your Data Science Portfolio: Project ideas to showcase and sharpen your skills.📊 Case Files: Success Stories from the ML World◘ Spotting Python Art vs. Multi-Million Dollar Creations: A fascinating test in AI-powered art valuation.◘ AI Takes Center Stage: How AI solutions are finding unique, transformative applications.◘ Excel Reporting’s Hidden Costs – A Fix Guide: Learn how optimized reporting can save resources.◘ Beyond RAG: Precision in Semantic Filtering: Improving precision with refined semantic techniques.◘ Aligning Preferences with AI – For Everyone: Discovering ways to enhance user alignment in AI-driven products.🌍 ML Headlines: Industry Buzz & Discoveries◘ Snowflake & CMU’s SuffixDecoding: A breakthrough in efficient token generation.◘ Sentence Transformers v3.3.0 by Hugging Face: What’s new in the latest release.◘ DeepMind’s AlphaFold 3 – Available Now: Explore the new codebase and on-demand server options.◘ Spotting Social Media Anomalies with AI: A novel approach to detecting volume changes in social data.◘ OpenFLAME by CMU Researchers: A federated, decentralized localization service for better data security.Stay tuned and stay inspired – there’s always something new to discover in the ever-evolving world of Data Science and Machine Learning!Take our weekly survey and get a free PDF copy of our best-selling book,"Interactive Data Visualization with Python - Second Edition."We appreciate your input and hope you enjoy the book!Share Your Insights and Shine! 🌟💬Cheers,Merlyn Shelley,Editor-in-Chief, Packt.📚 Packt Signature Series: Must-Reads & Author Insights➽ RAG-Driven Generative AI: This new title, RAG-Driven Generative AI, is perfect for engineers and database developers looking to build AI systems that give accurate, reliable answers by connecting responses to their source documents. It helps you reduce hallucinations, balance cost and performance, and improve accuracy using real-time feedback and tools like Pinecone and Deep Lake. By the end, you’ll know how to design AI that makes smart decisions based on real-world data—perfect for scaling projects and staying competitive! Start your free trial for access, renewing at $19.99/month.eBook $24.99 $35.99Print + eBook $43.99➽ Building Production-Grade Web Applications with Supabase: This new book is all about helping you master Supabase and Next.js to build scalable, secure web apps. It’s perfect for solving tech challenges like real-time data handling, file storage, and enhancing app security. You'll even learn how to automate tasks and work with multi-tenant systems, making your projects more efficient. By the end, you'll be a Supabase pro! Start your free trial for access, renewing at $19.99/month.eBook $15.99 $31.99Print + eBook $39.99➽ Python Data Cleaning and Preparation Best Practices: This new book is a great guide for improving data quality and handling. It helps solve common tech issues like messy, incomplete data and missing out on insights from unstructured data. You’ll learn how to clean, validate, and transform both structured and unstructured data—think text, images, and audio—making your data pipelines reliable and your results more meaningful. Perfect for sharpening your data skills! Start your free trial for access, renewing at $19.99/month.eBook $24.99 $35.99Print + eBook $44.99🔍 Model Breakdown: Unveiling the Algorithm of the Week⫸ Reranking Using Huggingface Transformers for Optimizing Retrieval in RAG Pipelines: This article demonstrates how to enhance RAG (Retrieval-Augmented Generation) pipelines with reranking using Huggingface Transformers and Sentence Transformers. By building on a basic RAG setup, the blog covers implementing and evaluating reranking to improve context accuracy and relevance, with linked code examples for easy integration.⫸ Vision Transformer with BatchNorm: This blog explores the impact of incorporating Batch Normalization (BatchNorm) into Vision Transformers (ViTs) to enhance training speed and stability, especially for medium-to-small datasets. Experimental results with MNIST data reveal BatchNorm’s potential benefits over traditional ViTs in faster convergence and resilience with higher learning rates.⫸ Fixie AI Introduces Ultravox v0.4.1: This blog introduces Fixie AI’s Ultravox v0.4.1, an open-source multi-modal AI model designed to enhance real-time conversational AI by reducing latency, improving context-aware interactions, and enabling multi-modal understanding across text, images, and more.⫸ FinSafeNet: Advancing Digital Banking Security with Deep Learning for Fraud Detection and Real-Time Transaction Protection. This blog discusses the rising importance of AI-driven cybersecurity in digital banking, highlighting FinSafeNet, a novel deep-learning model that enhances fraud detection. With optimized feature selection and dual-attention mechanisms, FinSafeNet outperforms traditional models, achieving high accuracy and efficiency in detecting transaction fraud.⫸ Nous Research Introduces Two New Projects: The Forge Reasoning API Beta and Nous Chat. This blog explores Nous Research’s Forge Reasoning API Beta and Nous Chat, both designed to improve AI’s real-time reasoning efficiency. By optimizing inference speed and scalability through the Hermes model, these tools aim to enhance conversational AI with faster, context-aware responses suitable for dynamic applications.🚀 Trendspotting: What's Next in Tech Trends⫸ Pushing the frontiers of audio generation - Google DeepMind: This blog highlights advancements in Google’s speech generation technology, enabling natural, multi-speaker dialogue in digital assistants. With innovations like NotebookLM Audio Overviews and Illuminate, Google enhances AI-driven dialogue with improved audio quality, efficiency, and speaker consistency for immersive, accessible user experiences.⫸ Introducing ChatGPT search: This blog highlights ChatGPT’s enhanced web search feature, offering timely answers with links to reliable sources, covering topics like weather, stocks, news, and more. Available for Plus, Team, and select users, it blends natural conversation with accurate, up-to-date information from trusted providers.⫸ Watermarking for AI Text and Synthetic Proteins: This blog examines the role of digital watermarking in countering misinformation and bioterrorism risks posed by large language models and generative protein design. It highlights watermarking’s potential to trace ownership and enhance security across digital and biological content.⫸ DeepSeek AI Releases JanusFlow: A Unified Framework for Image Understanding and Generation. This blog introduces JanusFlow, a unified AI framework by DeepSeek AI that combines image understanding and generation within a single model. Using a streamlined architecture, JanusFlow enhances multimodal efficiency, outperforming traditional models across various benchmarks without complex modifications.⫸ TensorOpera AI Releases Fox-1: A Series of Small Language Models (SLMs) that Includes Fox-1-1.6B and Fox-1-1.6B-Instruct-v0.1. This blog introduces Fox-1, TensorOpera AI’s efficient Small Language Model (SLM) series, designed to deliver large language model (LLM)-like capabilities with minimal resources. Fox-1’s innovative architecture and open-source accessibility make advanced natural language processing feasible for researchers and developers with limited computational power.⫸ OpenAI's Expected January Launch: AI Agents Set to Automate Everyday Life. This blog covers OpenAI’s upcoming AI agents, set to revolutionize automation by performing autonomous tasks for users. With adaptive learning and context awareness, these agents aim to streamline personal and professional tasks, though privacy and ethical concerns remain.🛠️ Platform Showdown: Comparing ML Tools & Services⫸ 7 Ways to Improve Your Data Cleaning Skills with Python: This blog offers seven essential Python techniques for improving data cleaning skills, focusing on handling invalid data, converting data types, encoding categorical variables, managing outliers, feature selection, scaling, and filling missing values. These methods streamline data preparation for accurate analysis and model building.⫸ Using Pandas and SQL Together for Data Analysis: This blog explains how to combine SQL and Python (via Pandas) for data management, highlighting SQL’s readability and native database handling alongside Python’s flexibility. The tutorial introduces PandaSQL to enable SQL-style querying of Pandas DataFrames, demonstrating streamlined workflows in data analysis.⫸ 5 No-Cost Learning Resources for LLM Agents: This blog highlights five free resources for learning about Large Language Model (LLM) agents, covering courses, bootcamps, and guides that teach foundational concepts, agent architectures, and real-world applications. These resources aim to help beginners and professionals alike stay current in the rapidly evolving field of LLM agents.⫸ Navigating AI Regulation: Balancing Innovation and Protection. This blog highlights five free resources for learning about Large Language Model (LLM) agents, covering courses, bootcamps, and guides that teach foundational concepts, agent architectures, and real-world applications. These resources aim to help beginners and professionals alike stay current in the rapidly evolving field of LLM agents.⫸ 7 Python Projects to Boost Your Data Science Portfolio: This blog outlines seven data science-focused Python projects designed to strengthen programming skills. Projects include automated data cleaning, ETL pipelines, data profiling packages, and CLI tools, all aimed at enhancing Python proficiency through real-world applications and best practices.📊 Success Stories: Real-World ML Case Studies⫸ Can You Tell Free Python Art from Multi-Million Dollar Pieces? This blog explores using Python for generative art inspired by Piet Mondrian and Josef Albers, focusing on creating unique, reproducible pieces. The author shares techniques for controlled randomness and color theory, encouraging readers to try their hand at generative art with accessible coding tools.⫸ Nobody Puts AI in a Corner! This blog explains how companies can effectively transform into AI-enabled businesses by learning from past digitalization and data science efforts. Through two anecdotes, it illustrates how a successful AI transformation requires integrating AI into core business functions, fostering cross-team communication, and leveraging industry knowledge to identify meaningful applications rather than relying solely on isolated AI initiatives.⫸ Reporting in Excel Could Be Costing Your Business More Than You Think — Here’s How to Fix It… This blog shares solutions to common reporting challenges faced by agencies, such as lengthy data compilation, limited Excel capabilities, and data inaccuracies. It outlines a workflow using Python in Deepnote for data cleaning, BigQuery for secure and efficient data storage, and Power BI for dynamic, interactive visualizations, streamlining the reporting process and enhancing data insights.⫸ Beyond RAG: Precision Filtering in a Semantic World. This blog delves into improving Retrieval-Augmented Generation (RAG) systems by incorporating outlier detection for efficient and accurate question filtering. Highlighting the limitations of standard retrieval methods, it introduces "Muzlin," a Python library for semantic filtering, to ensure questions align with available context, optimizing RAG performance in production environments.⫸ Preference Alignment for Everyone! This blog provides a detailed guide to Reinforcement Learning from Human Feedback (RLHF) as a method for preference alignment (PA) in large language models. By aligning model outputs with user preferences through human feedback, RLHF enhances user satisfaction, making AI interactions more relevant and reliable. The post includes practical implementation tips using tools like Hugging Face and Amazon SageMaker, offering readers a hands-on, replicable approach to integrating PA in AI systems.🌍 ML Newsflash: Latest Industry Buzz & Discoveries⫸ Researchers from Snowflake and CMU Introduce SuffixDecoding: This blog introduces SuffixDecoding, a model-free approach designed to speed up large language model (LLM) token generation. By leveraging suffix tree structures built from past outputs and current prompts, SuffixDecoding efficiently predicts and verifies token continuations without the need for draft models or additional decoding heads. This method improves throughput and reduces latency, proving valuable for complex applications like multi-stage pipelines and chat systems.⫸ Hugging Face Releases Sentence Transformers v3.3.0: This blog discusses Hugging Face's release of Sentence Transformers v3.3.0, highlighting advancements in CPU efficiency, prompt-based training, and model scalability. The update enhances NLP accessibility, making high-performance deployment feasible on resource-limited devices.⫸ DeepMind Released AlphaFold 3 Inference Codebase, Model Weights and An On-Demand Server: This blog discusses DeepMind’s release of AlphaFold 3, which extends structure prediction beyond proteins to multiple biomolecules, enabling broad research access and precision in drug discovery, biomolecular interactions, and therapeutic development with reduced computational barriers.⫸ Detecting Anomalies in Social Media Volume Time Series: This blog discusses using a residual-based approach to detect anomalies in social media conversation volumes, using Twitter data as an example. It covers seasonal adjustment, residual analysis, and real-time detection for effective social media monitoring.⫸ CMU Researchers Propose OpenFLAME: A Federated and Decentralized Localization Service. This blog introduces OpenFLAME, a decentralized, federated mapping service for indoor and private spaces that leverages DNS for scalable, privacy-preserving localization. It enables precise, adaptable localization without relying on centralized mapping providers.We’ve got more great things coming your way—see you soon!*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}

Summarize Texts Using the BART Model with Hugging Face Transformers, Fine-Tune T5 for QnA💥 FREE AI & ChatGPT Workshop (Limited time Offer) 🤯An AI-powered professional will earn 10x more. 💰An AI-powered founder will build & scale his company 10x faster 🚀An AI-first company will grow 50x more! 📊🚀Join this 3-hour AI Workshop (worth $399) - FREE for DataPro readers to learn AI strategies & hacks to 10X work output and grow your business.🗓️ Tomorrow | ⏱️ 10 AM ESTWith AI & Chatgpt, you will be able to:✅ Make smarter decisions based on data in seconds using AI✅ Automate daily tasks and increase productivity & creativity✅ Skyrocket your business growth by leveraging the power of AI✅ Save 1000s of dollars by using ChatGPT to simplify complex problems👉 Hurry! Click here to register (FREE for First 100 people only) 🎁Sponsored🗞️ Welcome to DataPro #119 – Your Weekly Data Science & ML Digest! 🌟Stay ahead in the world of AI and ML with this week’s top insights, strategies, and tools to elevate your projects and optimize performance. Here’s what’s trending:🔍 Model Spotlight: This Week’s Algorithm Insight★ Mastering Summarization: A guide to summarizing text with BART using Hugging Face Transformers.★ No-Code Wins: Discover the best no-code LLM app builders to streamline your workflows.★ Fresh Toolkit: Hugging Face’s new SmolTools—what you need to know.★ 3D Tracking Game-Changer: DELTA—an AI method that’s 10x faster at pixel tracking in 3D from monocular videos.★ Next-Level Embeddings: NVIDIA AI introduces MM-Embed.🚀 Exclusive for Packt Community: 50% Off Generative AI in Action!Join 25+ top AI experts and access 30+ sessions at our flagship event (Nov 11-13, LIVE). Public tickets are at 35% off, but you get 50% off—our best rate!Limited seats available prices rise by $200 once they're gone. Don’t wait!Book Now with Code BIGSAVE50🚀 Trending Now: Future Tech and Beyond★ T5 Fine-Tuning: How to fine-tune T5 for question answering tasks with Hugging Face Transformers.★ Understanding AI: A quick look at ANI, AGI, and ASI—three core types of artificial intelligence.★ Blueprints for Innovation: Create up-to-date generative AI apps with real-time vector embedding for Amazon MSK.★ Fish Agent Release: Check out Fish Agent v0.1 3B.★ Defense Llama: Scale AI and Meta’s new security initiative.🛠️ Tool Comparisons: ML Platforms Head-to-Head★ Critical Thinking Skills: 7 essential skills every data scientist needs.★ AI Regulation Guide: Navigating the fine line between innovation and protection.★ Meta’s AdaCache: A fresh tool for optimizing AI workflows.★ Model Depot: LLMWare’s latest contribution to model management.★ Hunyuan Model: Tencent’s powerful Hunyuan-MoE-A52B.★ AMD Goes Open Source: Details on the AMD OLMo release.📊 Case Studies: Real-World ML in Action★ MDAgents: A multi-agent framework enhancing medical decision-making with large language models.★ SMART Filtering: Improving NLP model evaluation with enhanced benchmarking.★ Hertz-Dev: Explore the open-source 8.5B audio model for real-time conversational AI.★ PII Masker: An essential open-source tool for safeguarding sensitive data.★ Scalable Chatbots: Building a context-aware chatbot using Amazon DynamoDB, Bedrock, and LangChain.🌍 ML Newsflash: Industry Highlights★ Free Learning Opportunity: Unlimited access to 365 Data Science courses until Nov 21.★ Python Certification: Learn Python and become a certified data analyst for free this week.★ Run Model Streamer: Run AI’s new open-source tool explained.★ MaskGCT: Dive into this state-of-the-art text-to-speech model.★ PyTorch/XLA 2.5 Updates: What’s new?★ BigQuery Prep Simplified: Meet the new AI-driven data preparation tool.Stay informed and inspired with DataPro’s latest curation—boost your skills, stay ahead, and make an impact!Take our weekly survey and get a free PDF copy of our best-selling book,"Interactive Data Visualization with Python - Second Edition."We appreciate your input and hope you enjoy the book!Share Your Insights and Shine! 🌟💬Cheers,Merlyn Shelley,Editor-in-Chief, Packt.📚 Packt Signature Series: Must-Reads & Author Insights➽ RAG-Driven Generative AI: This new title, RAG-Driven Generative AI, is perfect for engineers and database developers looking to build AI systems that give accurate, reliable answers by connecting responses to their source documents. It helps you reduce hallucinations, balance cost and performance, and improve accuracy using real-time feedback and tools like Pinecone and Deep Lake. By the end, you’ll know how to design AI that makes smart decisions based on real-world data—perfect for scaling projects and staying competitive! Start your free trial for access, renewing at $19.99/month.eBook $24.99 $35.99Print + eBook $43.99➽ Building Production-Grade Web Applications with Supabase: This new book is all about helping you master Supabase and Next.js to build scalable, secure web apps. It’s perfect for solving tech challenges like real-time data handling, file storage, and enhancing app security. You'll even learn how to automate tasks and work with multi-tenant systems, making your projects more efficient. By the end, you'll be a Supabase pro! Start your free trial for access, renewing at $19.99/month.eBook $15.99 $31.99Print + eBook $39.99➽ Python Data Cleaning and Preparation Best Practices: This new book is a great guide for improving data quality and handling. It helps solve common tech issues like messy, incomplete data and missing out on insights from unstructured data. You’ll learn how to clean, validate, and transform both structured and unstructured data—think text, images, and audio—making your data pipelines reliable and your results more meaningful. Perfect for sharpening your data skills! Start your free trial for access, renewing at $19.99/month.eBook $24.99 $35.99Print + eBook $44.99🔍 Model Breakdown: Unveiling the Algorithm of the Week⇝ How to Summarize Texts Using the BART Model with Hugging Face Transformers: This blog guides readers on using BART, a powerful tool for summarizing long texts into concise versions. It covers setting up the environment with Hugging Face Transformers and loading the model to create coherent summaries efficiently.⇝ Best No-Code LLM App Builders: This post highlights three open-source, no-code solutions—Flowise AI, Langflow, and Dify—that enable non-technical users to easily build and deploy AI applications using drag-and-drop interfaces and seamless integration with various LLMs.⇝ Hugging Face Releases SmolTools: This article explores Hugging Face's latest release of Smol-Tools, showcasing the compact yet powerful SmolLM2 model. It highlights the model's ability to perform efficient NLP tasks like summarization and rewriting while ensuring accessibility and performance.⇝ DELTA: A Novel AI Method that Efficiently (10x Faster) Tracks Every Pixel in 3D Space from Monocular Videos. This article covers DELTA, a novel method by UMass Amherst & MIT-IBM Watson AI Lab for efficient dense 3D tracking in videos. DELTA outperforms existing approaches by leveraging spatio-temporal attention and upsampling, achieving faster, more accurate results.⇝ NVIDIA AI Introduces MM-Embed: This article discusses NVIDIA's MM-Embed, a groundbreaking multimodal retriever achieving state-of-the-art results by handling text and image content seamlessly. MM-Embed improves cross-modal search performance, setting new standards for diverse, real-world information retrieval tasks.🚀 Trendspotting: What's Next in Tech Trends⇝ How to Fine-Tune T5 for Question Answering Tasks with Hugging Face Transformers: This article explains how to fine-tune the T5 model, a versatile text-to-text transformer, for question answering tasks using the Hugging Face and PyTorch libraries. It also guides readers through installing necessary tools and loading datasets.⇝ The Three Different Types of Artificial Intelligence – ANI, AGI and ASI: This article explains the three main types of AI: Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI), and Artificial Super Intelligence (ASI). It covers their capabilities, challenges, and potential impacts on technology and society.⇝ Build up-to-date generative AI applications with real-time vector embedding blueprints for Amazon MSK: This article explores building real-time AI applications using Amazon Bedrock and Amazon MSK to create vector embeddings, stored in OpenSearch Service, enabling Retrieval Augmented Generation (RAG). It emphasizes real-time data for accurate, up-to-date generative AI outputs.⇝ Fish Agent v0.1 3B Released: This article discusses Fish Agent v0.1 3B, a breakthrough Text-to-Speech system addressing complex linguistic challenges with its Dual Autoregressive architecture and Firefly-GAN vocoder. It bypasses G2P conversion, enhancing multilingual capabilities and delivering natural-sounding, high-quality speech synthesis.⇝ Scale AI and Meta Introduces Defense Llama: This article introduces Defense Llama, a collaborative project by Scale AI and Meta, designed as the first LLM for U.S. national security. It integrates specialized defense data, enhancing threat detection, secure communication, and strategic analysis capabilities.🛠️ Platform Showdown: Comparing ML Tools & Services⇝ 7 Critical Thinking Skills Needed in Data Science: This article lists and explains seven critical thinking skills essential for data scientists. It covers analytical abilities like pattern recognition and systems thinking, as well as practical skills such as problem decomposition and impact assessment for effective data analysis.⇝ Navigating AI Regulation: Balancing Innovation and Protection: This article highlights the need for balanced AI regulation that ensures ethical practices, privacy, and accountability without stifling innovation. It discusses challenges like algorithmic bias, data privacy, and safety risks, emphasizing global cooperation and risk-based frameworks for effective policies.⇝ Meta AI Introduces AdaCache: This article covers AdaCache, a training-free method developed by Meta AI and Stony Brook University to optimize video generation in diffusion transformers. By using adaptive caching and motion-based regularization, AdaCache enhances processing speed while maintaining high-quality output, addressing latency challenges efficiently.⇝ LLMWare Introduces Model Depot: This blog introduces LLMWare.ai’s Model Depot on Hugging Face, showcasing over 100 optimized Small Language Models (SLMs) for Intel PCs. It highlights support for OpenVINO and ONNX formats, enabling efficient, secure, on-device AI development and deployment.⇝ Tencent Releases Hunyuan-Large (Hunyuan-MoE-A52B) Model: This blog introduces Tencent's Hunyuan-Large, the largest open-source Transformer-based Mixture of Experts (MoE) model, featuring 389 billion parameters. It excels in NLP tasks and long-context processing, offering significant advancements in efficiency and scalability for the AI community.⇝ AMD Open Sources AMD OLMo: This blog discusses AMD's release of OLMo, a fully open-source 1B-parameter language model trained on AMD GPUs. It emphasizes OLMo's capabilities in NLP tasks, accessibility for developers, and its potential to democratize AI research and innovation.📊 Success Stories: Real-World ML Case Studies⇝ MDAgents: A Dynamic Multi-Agent Framework for Enhanced Medical Decision-Making with Large Language Models. This blog discusses MDAgents, a multi-agent framework developed by MIT, Google Research, and Seoul National University Hospital for medical decision-making. MDAgents dynamically assign LLMs based on task complexity, improving diagnostic accuracy across medical benchmarks through adaptive collaboration.⇝ SMART Filtering: Enhancing Benchmark Quality and Efficiency for NLP Model Evaluation. This blog covers SMART filtering, developed by Meta AI, Pennsylvania State University, and UC Berkeley, for improving NLP benchmark datasets by removing easy, contaminated, or redundant examples. This method enhances dataset quality, reduces computational costs, and maintains reliable model performance metrics for better evaluations.⇝ Meet Hertz-Dev: An Open-Source 8.5B Audio Model for Real-Time Conversational AI. This blog introduces Hertz-Dev, an open-source 8.5 billion parameter model for real-time conversational AI by Standard Intelligence Lab. It achieves low latency on a single RTX 4090 GPU, making high-performance audio modeling accessible and efficient for diverse developers.⇝ Meet PII Masker: An Open-Source Tool for Protecting Sensitive. This blog introduces PII Masker, an advanced open-source tool by HydroXai for protecting sensitive data using AI and NLP. It automates the detection and masking of PII, ensuring privacy compliance while maintaining data usability and minimizing false positives.⇝ Build a scalable, context-aware chatbot with Amazon DynamoDB, Amazon Bedrock, and LangChain: This blog outlines how to build scalable, context-aware chatbots using Amazon DynamoDB, LangChain, and Amazon Bedrock. It details managing chat history with DynamoDB for seamless user interactions and creating intelligent responses through LangChain's integration, ensuring coherent and personalized conversations.🌍 ML Newsflash: Latest Industry Buzz & Discoveries⇝ Free Data and AI Courses with 365 Data Science—Unlimited Access until Nov 21: This blog highlights 365 Data Science's annual free access initiative, providing users with unrestricted learning resources, expert-led courses, and certifications to enhance career prospects in data science and AI. It aims to democratize education and bridge the skills gap in a competitive job market.⇝ Learn Python and get Certified as a Data Analyst for Free this Week! This blog highlights DataCamp's Free Access Week from November 4th to 10th, offering users unlimited learning at no cost. It features popular courses for data analysis and science in Python and R, providing opportunities for certification and skill-building in data analytics.⇝ Run AI Open Sources Run:ai Model Streamer: This blog highlights Run AI's release of Model Streamer, an open-source tool designed to drastically reduce model loading times by up to six times. It supports various storage solutions and simplifies deployment, enhancing productivity and the efficiency of real-world AI applications.⇝ MaskGCT: A New Open State-of-the-Art Text-to-Speech Model. This blog introduces MaskGCT, an innovative open-source TTS model that overcomes traditional alignment and duration prediction challenges using a non-autoregressive, two-stage framework. Trained on 100,000 hours of data, it excels in naturalness, speed, and versatile applications like voice cloning and emotional synthesis.⇝ What’s new with PyTorch/XLA 2.5: This blog discusses the updates in PyTorch/XLA 2.5, including API streamlining for easier use with PyTorch, improvements to the torch_xla.compile function for better debugging, and experimental TPU support in vLLM. These changes enhance the developer experience and broaden deployment capabilities.⇝ Introducing AI-driven BigQuery data preparation: This blog introduces BigQuery data preparation, an AI-powered solution that simplifies data preparation by automating tasks like data cleansing and transformation. It features visual data pipelines and AI-driven suggestions, enhancing efficiency and ensuring reliable, actionable insights for users in Google Cloud.We’ve got more great things coming your way—see you soon!*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}

Gemini Models Hit GitHub Copilot, Python One-Liners for Data Cleaning, Python for Proximity Mapping200+ hours of research on AI tools & hacks packed in 3 hoursThis free 3-hour Training on AI & ChatGPT (worth $399) will help you become a master of 20+ AI tools & prompting techniques and save 16 hours/week.Get it now for absolutely free! (for first 100 users only) 🎁You will learn how to:➣ Build business that make $10,000 by just using AI tools➣ Make quick & smarter decisions using AI-led data insights➣ Write emails, content & more in seconds using AI➣ Solve complex problems, research 10x faster & save 16 hours every weekRegister & save your seat now! (100 free seats only)SponsoredWelcome to DataPro #118 – Your Weekly Data Science & ML Wizardry! 🌟Stay sharp in the fast-evolving world of data science with this week’s essential strategies, tools, and trends. We’ve handpicked the best to supercharge your projects, refine accuracy, and amp up performance. Ready for this week’s power-ups? Let’s go!🚨 Packt Conference Alert! 🚨Stay at the forefront of AI innovation! 🚀 Join us for 3 action-packed days of LIVE sessions with 20+ top experts and unleash the full power of Generative AI at our upcoming conference. Don’t miss out - Claim your spot today!🔍 Algorithm Insight: Model of the Week Unveiled➣Gemini Models Hit GitHub Copilot: Dive into code generation like never before with Gemini models, now integrated in GitHub Copilot through Google Cloud’s partnership.➣SimpleQA from OpenAI: A new benchmark tool to measure the factual accuracy of language models.➣Theory of Mind in AI: Evaluating the latest with SimpleToM, a new tool testing language models’ understanding of human perspectives.➣Meta AI’s LongVU: Tackling long video comprehension with a new multimodal language model.➣JetBrains Introduces CoqPilot: A Plugin for LLM-Based Proof Generation.➣Jupyter Releaser: Streamlining software releases for Jupyter tools just got easier.🚀 Tech Trend Radar: What's Making Waves?➣LLMs for Chunked Retrieval: How to leverage LLMs for smarter, chunk-based information recall.➣OmniParser by Microsoft AI: Convert UI screenshots to structured data on Hugging Face.➣Hawkish 8B Financial Model: Outperforming in finance tests, this model aces CFA Level 1 exams.➣Gen-AI Safety Stack: A guide to safety strategies for text-to-image model applications.➣Equation Solving in Python: A must-read on closed-form versus numerical solutions.🛠️ Tool Time: Comparing Platforms & Services➣Cohere’s Aya Expanse: A powerful multilingual model suite closing the language gap in AI.➣Meta AI’s NotebookLlama: An open-source alternative to Google’s NotebookLM, now available.➣AI for Screen Interaction: Explore Claude 3.5’s new screen navigation capabilities.➣Text Embeddings with Amazon RDS & Bedrock: Seamlessly embed and retrieve text data from Amazon RDS using Amazon’s Bedrock.➣Custom Observability Solution: Track, log, and improve generative AI applications with Bedrock.📊 Real-World Impact: Success Stories & Case Studies➣Python One-Liners for Data Cleaning: 10 concise solutions for everyday data wrangling.➣2024’s Top Python Libraries: Must-have Python tools for data science this year.➣Automating Model Selection with LLMs: Streamlining model testing and tuning.➣5 Tips to Optimize Language Models: Quick techniques for better model performance.➣Lessons Beyond AI: Three crucial takeaways from a recent data science conference.🌍 ML Newsflash: Industry Discoveries & Updates➣Hugging Face Models on Mobile: A step-by-step guide to deploying Hugging Face models on mobile.➣Python for Proximity Mapping: Learn how to create distance maps in Python for quick insights.➣Data Leakage Alert: Key practices to prevent leaks during data preprocessing.➣In-Depth RAG Guide: Understand Retrieval Augmented Generation with a breakdown of each component.➣Beyond Basic Attention in Transformers: Analyzing positional embedding techniques for improved model accuracy.Dive into this week’s DataPro and stay on top of everything that’s shaping the world of Data Science & Machine Learning!Take our weekly survey and get a free PDF copy of our best-selling book,"Interactive Data Visualization with Python - Second Edition."We appreciate your input and hope you enjoy the book!Share Your Insights and Shine! 🌟💬Cheers,Merlyn Shelley,Editor-in-Chief, Packt.📚 Packt Signature Series: Must-Reads & Author Insights➽ RAG-Driven Generative AI: This new title, RAG-Driven Generative AI, is perfect for engineers and database developers looking to build AI systems that give accurate, reliable answers by connecting responses to their source documents. It helps you reduce hallucinations, balance cost and performance, and improve accuracy using real-time feedback and tools like Pinecone and Deep Lake. By the end, you’ll know how to design AI that makes smart decisions based on real-world data—perfect for scaling projects and staying competitive! Start your free trial for access, renewing at $19.99/month.eBook $24.99 $35.99Print + eBook $43.99➽ Building Production-Grade Web Applications with Supabase: This new book is all about helping you master Supabase and Next.js to build scalable, secure web apps. It’s perfect for solving tech challenges like real-time data handling, file storage, and enhancing app security. You'll even learn how to automate tasks and work with multi-tenant systems, making your projects more efficient. By the end, you'll be a Supabase pro! Start your free trial for access, renewing at $19.99/month.eBook $15.99 $31.99Print + eBook $39.99➽ Python Data Cleaning and Preparation Best Practices: This new book is a great guide for improving data quality and handling. It helps solve common tech issues like messy, incomplete data and missing out on insights from unstructured data. You’ll learn how to clean, validate, and transform both structured and unstructured data—think text, images, and audio—making your data pipelines reliable and your results more meaningful. Perfect for sharpening your data skills! Start your free trial for access, renewing at $19.99/month.eBook $24.99 $35.99Print + eBook $44.99🔍 Model Breakdown: Unveiling the Algorithm of the Week➽ Gemini Models on GitHub Copilot: GitHub and Google Cloud’s partnership introduces Gemini 1.5 Pro to GitHub, enhancing AI-driven code generation, analysis, and optimization for developers. The Gemini model, with a two-million-token context window, will integrate into GitHub Copilot, Google AI Studio, Vertex AI, and popular IDEs.➽ OpenAI Introduces SimpleQA: AI Benchmark for Measuring the Factuality of Language Models. The blog introduces SimpleQA, a factuality benchmark for evaluating how accurately language models answer short, fact-seeking questions. SimpleQA emphasizes correctness, topic diversity, and difficulty for advanced models. Built with rigorous quality checks, it helps researchers gauge model performance and reduce “hallucinations” in AI responses.➽ SimpleToM: Evaluating Applied Theory of Mind Capabilities in Large Language Models. The blog discusses SimpleToM, a dataset developed to assess Theory of Mind (ToM) in large language models (LLMs) through realistic scenarios. Unlike prior methods, it evaluates nuanced mental state inferences and behavior judgments, revealing gaps in LLMs’ understanding and application of social reasoning in real-world situations.➽ Data Minimization Does Not Guarantee Privacy: The blog explains the data minimization principle in machine learning, emphasizing the need to collect only essential data to reduce privacy risks, as outlined by global data protection laws. It discusses challenges in operationalizing this principle due to inherent data correlations and highlights privacy audits, using adversarial attacks, to identify vulnerabilities.➽ Meta AI Releases LongVU: A Multimodal Large Language Model that can Address the Significant Challenge of Long Video Understanding. The blog highlights Meta AI's release of LongVU, a Multimodal Large Language Model designed to tackle the challenges of long video understanding. By using adaptive compression techniques and cross-modal queries, LongVU reduces redundant frames and tokens, enabling efficient processing of hour-long videos within limited context lengths, thereby advancing video analysis in AI.➽ JetBrains Researchers Introduce CoqPilot: A Plugin for LLM-Based Generation of Proofs. The blog introduces CoqPilot, a VS Code extension from JetBrains that automates Coq proof generation. By using LLMs like GPT-4 and tools like CoqHammer, CoqPilot fills proof gaps, verifies solutions, and replaces incomplete proofs. This integration streamlines proof creation, enhancing efficiency in software reliability and formal verification tasks.➽ Jupyter Releaser: Streamlining Software Releases for the Jupyter Ecosystem. The blog covers Jupyter Releaser, a tool launched by the Jupyter team to streamline release management across Jupyter projects. By automating tasks like changelog creation and artifact publishing via GitHub Actions, Jupyter Releaser reduces errors, speeds up releases, and promotes consistency, benefiting the broader open-source development community.🚀 Trendspotting: What's Next in Tech Trends➽ How and Why to Use LLMs for Chunk-Based Information Retrieval. The article explores using Large Language Models (LLMs) like GPT-4 for chunk-based information retrieval. By utilizing hybrid search techniques—combining term frequency algorithms and vector-based search—LLMs identify relevant text chunks. Despite improving retrieval, issues like irrelevant chunk selection persist, potentially misleading LLM responses in systems like RAG (Retrieval-Augmented Generation).➽ Microsoft AI Releases OmniParser Model on HuggingFace: A Compact Screen Parsing Module that can Convert UI Screenshots into Structured Elements. OmniParser by Microsoft enables GUI interaction for AI by interpreting interface elements from screenshots without HTML or metadata. Using vision-based detection, icon description, and OCR, it enhances AI usability across platforms, boosting accuracy in interface tasks and advancing applications in automation and accessibility.➽ Meet Hawkish 8B: A New Financial Domain Model that can Pass CFA Level 1 and Outperform Meta Llama-3.1-8B-Instruct in Math & Finance Benchmarks. The article introduces Hawkish 8B, a finance-focused AI model excelling in financial analysis and quantitative tasks. With specialized training in economics and market analysis, Hawkish 8B surpasses other models in benchmarks and even passes CFA Level 1, aiding finance professionals.➽ Gen-AI Safety Landscape: A Guide to the Mitigation Stack for Text-to-Image Models: The article covers Text-to-Image (T2I) AI models like Latent Diffusion Models, detailing capabilities like inpainting and associated risks, including generating inappropriate content. It emphasizes a robust safety mitigation stack across training, fine-tuning, and post-deployment to minimize harmful outputs and ethical concerns.➽ Solving Equations in Python: Closed-Form vs Numerical: The article explores when closed-form solutions are possible in mathematical models, such as Kepler’s orbital equation, and why numerical methods are often needed. Using Python’s SymPy, it examines equations to build intuition around solvable forms and complexities that defy simple algebraic solutions.➽ Demystifying Azure Storage Account Network Access: The article details network access control for Azure storage accounts within medallion architecture, focusing on using service endpoints and private endpoints. It explains setup configurations, firewall rules, and network security groups (NSGs) to securely enable data access for virtual machines while preventing unauthorized access.🛠️ Platform Showdown: Comparing ML Tools & Services➽ Cohere for AI Releases Aya Expanse (8B & 32B): A State-of-the-Art Multilingual Family of Models to Bridge the Language Gap in AI. The article introduces Aya Expanse by Cohere for AI, an open-weight, multilingual language model family addressing underrepresentation in NLP. Designed to support low-resource languages, Aya Expanse achieves high accuracy on multilingual benchmarks, promoting inclusivity and equitable access to AI-driven tools across diverse linguistic communities.➽ Meta AI Silently Releases NotebookLlama: An Open Version of Google's NotebookLM. The article introduces Meta's NotebookLlama, an open-source alternative to Google’s NotebookLM, integrating LLMs into a notebook interface for accessible, scalable data analysis and documentation. NotebookLlama offers customizable deployment, enhances code-writing and documentation, and empowers the AI community with a flexible, community-driven tool.➽ Computer Use and AI Agents: A New Paradigm for Screen Interaction: The article explores recent advancements in multimodal AI agents from Anthropic, Microsoft, and Apple. These agents enhance computer and mobile screen interaction using technologies like Anthropic’s Claude 3.5, Microsoft’s OmniParser, and Apple’s Ferret-UI, highlighting varied approaches for parsing screens and performing actions, albeit with ongoing challenges.➽ Embed textual data in Amazon RDS for SQL Server using Amazon Bedrock: The article explains how to generate vector embeddings from Wikipedia data stored in an Amazon RDS SQL Server database. Using Amazon Bedrock and Amazon SageMaker, the solution integrates embeddings into SQL Server for similarity search in generative AI applications, streamlining analysis through AWS’s managed AI services.➽ Empower your generative AI application with a comprehensive custom observability solution: The article introduces an observability and evaluation solution for Amazon Bedrock to enhance generative AI applications. By integrating decorators in application code, this solution captures logs and metrics, supporting Retrieval Augmented Generation (RAG) evaluations and enabling proactive monitoring, quality improvement, and secure data handling across AI workflows.📊 Success Stories: Real-World ML Case Studies➽ 10 Useful Python One-Liners for Data Cleaning: The article provides Python one-liners for common data cleaning tasks like handling duplicates, validating formats, managing missing values, and scaling numbers. It guides users in cleaning a sample dataset to prepare it for analysis, covering essentials like email validation, date standardization, and whitespace trimming.➽ 10 Essential Python Libraries for Data Science in 2024: The article covers ten essential Python libraries for data science, each specializing in a critical task like data collection (Scrapy), manipulation (pandas), visualization (Matplotlib), machine learning (scikit-learn), and deployment (Flask). These libraries streamline end-to-end workflows, making data science more accessible and efficient.➽ Selection and Experimentation Automation with LLMs: The article demonstrates how to automate model selection and experimentation using large language models (LLMs). By applying LLMs like GPT-4 with Scikit-Learn, the code automates model evaluation, selects the best-performing model, and even suggests hyperparameters for tuning. This approach streamlines model experimentation in data science.➽ 5 Tips for Optimizing Language Models: The article provides five essential tips for optimizing language models: using prompt engineering to refine model responses, applying Retrieval Augmented Generation (RAG) for contextual accuracy, fine-tuning for task specificity, adjusting hyperparameters to enhance performance, and compressing models for efficiency and accessibility across various platforms.➽ Three Crucial Data Lessons That I Learned from a Data Conference That’s Not Related to AI. The article shares insights from a data conference, emphasizing cost control, effective data translation, and cross-department collaboration to boost data team ROI. Practical tips include using cost-monitoring dashboards, fostering data literacy, and aligning data projects with strategic business goals.➽ How Prefab scales with Spanner’s PostrgeSQL interface: Prefab uses Google Cloud Spanner’s PostgreSQL interface for its impressive scalability, simplicity, and cost-effectiveness. Spanner offers the robustness of PostgreSQL with high availability, strong ACID compliance, and horizontal scaling, making it ideal for Prefab's feature flagging and dynamic logging services.🌍 ML Newsflash: Latest Industry Buzz & Discoveries➽ How to Deploy Hugging Face Models on Mobile Devices: This guide covers deploying Hugging Face models on mobile by converting models like DistilBERT into ONNX format, then quantizing to reduce file size for mobile compatibility. The article also demonstrates testing and setup for Android deployment, enabling efficient and scalable use of machine learning on mobile devices.➽ Building Interactive Data Science Applications with Python:This article details building interactive data science applications using Python libraries like Streamlit, Gradio, Dash, and Panel. It explains creating engaging apps with features like user inputs, feedback, and multimedia elements, and includes an example dashboard that visualizes U.S. population data from 2010–2019.➽ How to Make Proximity Maps with Python: This blog post walks through creating a "distance from" map using Python to calculate distances between universities in the Southeastern Conference (SEC) for college football. It details coding steps to visualize travel distances from one school to others on a contour map, ideal for analyzing team travel or other location-based data.➽ Data Leakage in Preprocessing: This article addresses data leakage in machine learning, where test data unintentionally influences training data during preprocessing. Common issues include imputing missing values using the mean of the entire dataset, blending test insights into training, which skews model performance.➽ The Ultimate Guide to RAGs — Each Component Dissected: This blog explores Retrieval Augmented Generation (RAG) in Large Language Models, where relevant data is first retrieved from external sources, then combined with user queries to produce more accurate responses. The RAG approach helps improve accuracy, reduce hallucinations, and provide up-to-date information efficiently.➽ Beyond Attention: How Advanced Positional Embedding Methods Improve upon the Original Approach in Transformer Architecture. This article explains how the Transformer architecture improved AI models by enabling faster processing and capturing long-range relationships in data through self-attention. Positional embeddings, like sinusoidal and learned encodings, help maintain order, making models work well across different data types.We’ve got more great things coming your way—see you soon!*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}

Product-Oriented ML, ML Metamorphosis, Optimize ALBERT for Mobile Deployment with Hugging Face Trans🚀 The Most Awaited 2-for-1 Deal Drops Tomorrow! 🚀Unlock our 2-for-1 offer at Generative AI in Action (Nov 11-13) and bring a friend, colleague, or your team to double the learning experience.🗓 Sale Starts: Tomorrow, Friday, Oct 25, 10 AM ET⏳ Duration: 24 hours onlyDon’t miss out—mark your calendar and get ready to grab this exclusive deal!CTA: Join 25+ AI Experts, 30+ Sessions & 1000+ Tech ProsWelcome to DataPro #117 – Your Weekly Data Science & ML Wizardry! 🌟Stay on top of AI and ML breakthroughs with this week’s hottest tools, trends, and strategies. Ready to supercharge your projects? Let’s jump in! 🚀🔍 Model of the Week: Cracking Open AI Innovations✦ Activation Steering by Microsoft: Discover a game-changing method to enhance instruction-following in LLMs.✦ Stable Diffusion 3.5: The latest release from Stability AI promises faster, more accurate image generation.✦ FunnelRAG: Supercharge your AI with this innovative approach to improve retrieval in RAG systems.✦ Meet SynPO: A cutting-edge technique using synthetic data for smarter model alignment.✦ Moonshine: Fast, accurate, lightweight speech recognition for edge devices.🚀 Tech Trends on the Rise✦ LayerSkip by Meta AI: Speed up LLM inference with this breakthrough in AI architecture.✦ IBM’s Granite 3.0 Models: Power your enterprise AI with these robust new models.✦ OMat24 Dataset by Meta AI: The biggest open inorganic materials dataset, ready for your next project.✦ Meta Spirit LM: Explore the future of text and speech with this open-source multimodal model.✦ Generative AI in Retail: How AI and data are transforming customer experiences.🛠️ Tools & Techniques Showdown✦ 5 Hidden Data Transformation Gems: Unveil new techniques for cleaner, faster analysis.✦ Top 10 GitHub Repos for NLP: Essential resources to master natural language processing.✦ Generative AI for Devs: Speed up software development with AI-driven coding tools.✦ Optimizing ALBERT for Mobile: Learn how to deploy Hugging Face Transformers efficiently on mobile.✦ Streamline Teamwork with Monday.com: Unlock smoother collaboration for data science projects.📊 Real-World Wins: ML Success Stories✦ OpenAI & Lenfest Fellowship: Learn how AI is shaping the future of journalism.✦ ML Metamorphosis: Discover how chaining models leads to breakthrough results.✦ Key Roles in Fraud Prediction: A deep dive into the people behind successful fraud detection with ML.✦ Mastering Back-of-the-Envelope Math: Quick estimations for better data-driven decisions.✦ Building Product-Oriented ML: From concept to product—guidance for data scientists.✦ Amazon Q Developer for AWS Lambda: New tools for faster, smarter code development.🌍 ML Newsflash: Hot Off the Press✦ The AWS Bedrock Tutorial: Everything you need to set up for AWS success.✦ Relational Deep Learning for Self-Service AI: Make ML easier with relational databases.✦ Why Scaling Works: Insights on inductive biases vs. scaling up models.✦ Optimizing AI Models on AWS Inferentia & Trainium: Best practices for faster results.✦ Chunking Documents with LLMs: Unlocking knowledge, one chunk at a time.Stay sharp, stay curious, and stay ahead with DataPro!Take our weekly survey and get a free PDF copy of our best-selling book,"Interactive Data Visualization with Python - Second Edition."We appreciate your input and hope you enjoy the book!Share Your Insights and Shine! 🌟💬Cheers,Merlyn Shelley,Editor-in-Chief, Packt.📚 Packt Signature Series: Must-Reads & Author Insights➽ RAG-Driven Generative AI: This new title, RAG-Driven Generative AI, is perfect for engineers and database developers looking to build AI systems that give accurate, reliable answers by connecting responses to their source documents. It helps you reduce hallucinations, balance cost and performance, and improve accuracy using real-time feedback and tools like Pinecone and Deep Lake. By the end, you’ll know how to design AI that makes smart decisions based on real-world data—perfect for scaling projects and staying competitive! Start your free trial for access, renewing at $19.99/month.eBook $24.99 $35.99Print + eBook $29.99 $43.99➽ Building Production-Grade Web Applications with Supabase: This new book is all about helping you master Supabase and Next.js to build scalable, secure web apps. It’s perfect for solving tech challenges like real-time data handling, file storage, and enhancing app security. You'll even learn how to automate tasks and work with multi-tenant systems, making your projects more efficient. By the end, you'll be a Supabase pro! Start your free trial for access, renewing at $19.99/month.eBook $15.99 $31.99Print + eBook $27.98 $39.99➽ Python Data Cleaning and Preparation Best Practices: This new book is a great guide for improving data quality and handling. It helps solve common tech issues like messy, incomplete data and missing out on insights from unstructured data. You’ll learn how to clean, validate, and transform both structured and unstructured data—think text, images, and audio—making your data pipelines reliable and your results more meaningful. Perfect for sharpening your data skills! Start your free trial for access, renewing at $19.99/month.eBook $24.99 $35.99Print + eBook $30.99 $44.99🔍 Model Breakdown: Unveiling the Algorithm of the Week➽ Microsoft AI Introduces Activation Steering: A Novel AI Approach to Improving Instruction-Following in Large Language Models. This blog discusses the limitations of large language models in following detailed instructions during text generation and introduces "activation steering," a new method that improves adherence to constraints without retraining models, enhancing their flexibility and precision.➽ Stability AI Releases Stable Diffusion 3.5: Stable Diffusion 3.5 Large and Stable Diffusion 3.5 Large Turbo. This blog covers the release of Stable Diffusion 3.5, highlighting its improved image generation capabilities, adaptability for different user needs, and efficiency on consumer hardware. It emphasizes Stability AI’s focus on accessibility through flexible variants and permissive licensing.➽ FunnelRAG: A Novel AI Approach to Improving Retrieval Efficiency for Retrieval-Augmented Generation. This blog introduces Retrieval-Augmented Generation (RAG) and its role in enhancing language models by integrating external knowledge sources. It highlights FunnelRAG, a progressive retrieval method that improves efficiency and accuracy by refining data in stages, addressing challenges in large-scale information retrieval.➽ Meet SynPO: A Self-Boosting Paradigm that Uses Synthetic Preference Data for Model Alignment. This blog discusses SynPO (Synthetic Preference Optimization), a technique for improving LLMs' alignment with human preferences using self-generated synthetic data. SynPO reduces reliance on human annotations, enabling scalable, iterative improvement in model performance through synthetic feedback loops.➽ Moonshine: A Fast, Accurate, and Lightweight Speech-to-Text Models for Transcription and Voice Command Processing on Edge Devices. This blog discusses the introduction of Moonshine speech recognition models, which outperform traditional models like Whisper by using a variable-length encoder to reduce latency and computational demands. These models are faster, more efficient, and highly accurate, even on low-resource devices.🚀 Trendspotting: What's Next in Tech Trends➽ Meta AI Releases LayerSkip: A Novel AI Approach to Accelerate Inference in Large Language Models (LLMs). This blog introduces LayerSkip, a novel solution for accelerating large language model inference. It combines layer dropout, early exit loss, and self-speculative decoding to reduce computational and memory demands while maintaining high accuracy, offering significant efficiency improvements for practical AI deployment.➽ IBM Releases Granite 3.0 2B and 8B AI Models for AI Enterprises: This blog introduces IBM's Granite 3.0 AI models, designed for enterprises seeking secure, adaptable, and transparent AI solutions. These models excel in natural language processing, offer enhanced decision-making, and integrate with IBM's watsonx platform, making them ideal for privacy-focused, efficient AI deployment in diverse enterprise environments.➽ Meta AI Releases Meta’s Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models: This blog discusses the release of Meta's Open Materials 2024 (OMat24) dataset, containing over 110 million DFT calculations, and the EquiformerV2 model, which excels in predicting material properties. These resources aim to accelerate AI-driven materials discovery, addressing challenges in global issues like climate change and next-generation computing.➽ Meta AI Releases Meta Spirit LM: An Open Source Multimodal Language Model Mixing Text and Speech: This blog highlights Meta Spirit LM, an open-source multimodal language model that integrates text and speech at the word level, addressing expressivity limitations in traditional TTS systems. With its ability to generate natural and emotion-driven speech, it represents a significant leap in AI-driven multimodal applications, including conversational agents and virtual assistants.➽ How generative AI and data are redefining retail experiences? This blog discusses how generative AI is revolutionizing the retail and consumer goods industry by improving customer service, automating product marketing, and enabling hyper-personalized shopping experiences. Companies like TVG, DoorDash, and Orbit Irrigation are leveraging AI tools like Amazon Bedrock to enhance operations, drive growth, and improve customer satisfaction.🛠️ Platform Showdown: Comparing ML Tools & Services➽ 5 Lesser-Known Data Transformation Techniques for Better Analysis: This blog covers five lesser-known data transformation techniques—Box-Cox, Yeo-Johnson, Rank, Reciprocal, and Binning transformations—that can enhance data analysis by improving normality, managing outliers, and reducing skewness. These techniques offer more flexibility and precision for various data preprocessing tasks.➽ 10 GitHub Repositories to Master Natural Language Processing (NLP): This blog explores ten essential GitHub repositories for mastering Natural Language Processing (NLP). These repositories provide valuable resources such as tutorials, frameworks, courses, and projects to help users build and improve NLP models, including popular libraries like Hugging Face's Transformers, spaCy, and more.➽ Generative AI for Software Development - DeepLearning.AI: This blog highlights the "Generative AI for Software Development" course, led by former Google AI lead Laurence Moroney. The course equips developers with skills to integrate generative AI tools like GitHub Copilot and ChatGPT into real-world software development. Learners will enhance coding efficiency, improve code quality, and develop innovative solutions through hands-on projects. By mastering Large Language Models (LLMs), participants can streamline their development workflow and earn a Skill Certificate from DeepLearning.AI, demonstrating their proficiency in using AI-powered tools.➽ How to Optimize ALBERT for Mobile Deployment with Hugging Face Transformers: This blog tutorial guides you through optimizing the ALBERT model for mobile deployment by using techniques like quantization, pruning, and converting the model to ONNX format. These methods help reduce model size, improve performance, and enhance efficiency on resource-limited mobile devices, while maintaining high accuracy.➽ Streamlining Data Science Projects: How to Use Monday.com for Efficient Team Collaboration. This article discusses how Monday.com can streamline project management for data science teams by offering a centralized platform for collaboration, tracking progress, and managing workflows. It helps teams stay organized by integrating tools like GitHub and Slack, providing real-time data tracking, and enabling custom visual workflows. Monday.com's automation features, transparency, and flexibility in adapting to agile approaches make it a game-changer for teams handling multiple data projects simultaneously.📊 Success Stories: Real-World ML Case Studies➽ OpenAI and the Lenfest Institute AI Collaborative and Fellowship program: This blog discusses the collaboration between The Lenfest Institute, OpenAI, and Microsoft to support local journalism through AI-driven business sustainability. Selected newsrooms will receive grants and AI fellows to implement AI technologies and share innovations across the industry.➽ ML Metamorphosis: Chaining ML Models for Optimized Results. This blog explores the concept of "ML metamorphosis," a process that improves machine learning model performance by chaining multiple models together. Techniques like knowledge distillation, model compression, and rule extraction help create more efficient and accurate models.➽ Key Roles in a Fraud Prediction Project with Machine Learning: This blog explains the various roles involved in developing machine learning projects, such as project managers, fraud analysts, data engineers, data scientists, and MLOps engineers, and how their collaboration ensures the successful implementation and delivery of ML solutions.➽ Mastering Back-of-the-Envelope Math Will Make You a Better Data Scientist: This blog explores how quick-and-dirty estimates, like Enrico Fermi’s during the first nuclear bomb test, can be valuable in decision-making. It emphasizes structured thinking, simplicity, and getting "accurate enough" results for business decisions.➽ Product-Oriented ML: A Guide for Data Scientists. This blog outlines how to plan successful machine learning (ML) projects by defining clear problem statements, aligning with business goals, setting functional and non-functional requirements, and fostering cross-functional collaboration to avoid common pitfalls in ML development.➽ Introducing the new Amazon Q Developer experience in AWS Lambda: This blog highlights the integration of Amazon Q Developer, an AI-powered assistant, into AWS Lambda’s new code editor. The tool offers real-time code suggestions, chat assistance, and troubleshooting features to enhance coding efficiency and streamline debugging for developers.🌍 ML Newsflash: Latest Industry Buzz & Discoveries➽ The AWS Bedrock Tutorial I Wish I Had: Everything You Need to Know to Prepare Your Machine for AWS Infrastructure. This blog introduces a multi-part series on building full-stack AI apps with AWS Bedrock, React, and Node.js. It guides readers through AWS setup, permissions, and integrating GenAI tools for creating a fully functional language translation app.➽ Self-Service ML with Relational Deep Learning. This blog introduces Relational Deep Learning (RDL), an approach that bypasses traditional feature engineering by learning directly from relational databases. It explores RDL's potential in complex, real-world datasets, highlighting its strengths and challenges.➽ Why Scaling Works: Inductive Biases vs The Bitter Lesson. This blog explores the power of scaling in deep learning, demonstrating how larger models with more data consistently outperform others in tasks like image generation and language modeling, illustrated through a toy spiral classification problem.➽ AI Model Optimization on AWS Inferentia and Trainium: This blog discusses optimizing machine learning workloads on AWS Inferentia chips using the AWS Neuron SDK, focusing on performance improvements in training models like Vision Transformers through PyTorch, OpenXLA, and Neuron-specific techniques.➽ Efficient Document Chunking Using LLMs: Unlocking Knowledge One Block at a Time. This article explains how to use large language models (LLMs) like GPT-4o to chunk documents into meaningful segments, where each chunk represents a unified idea, aiding efficient knowledge base creation and organization.We’ve got more great things coming your way—see you soon!*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{line-height:0;font-size:75%} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}

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40+ Cool AI Tools, Inheritune, Rhymes AI’s Aria, Create Podcasts with NotebookLM, Falcon 2 11BLooking to build, train, deploy, or implement Generative AI?Meet Innodata — offering high-quality solutions for developing and implementing industry-leading generative AI, including:➤ Diverse Golden Datasets➤ Supervised Fine-Tuning Data➤ Human Preference Optimization (e.g. RLHF)➤ RAG Development ➤ Model Safety, Evaluation, & Red Teaming ➤ Data Collection, Creation, & Annotation ➤ Prompt Engineering With 5,000+ in-house SMEs and expansion and localization supported across 85+ languages,Innodata drives AI initiatives for enterprises globally.Learn More!SponsoredWelcome to DataPro #116 – Your Weekly Dose of Data Magic! 🌟Stay at the cutting edge of data engineering, data science, and AI! This week’s newsletter delivers the latest tools, insights, and strategies you need to accelerate your workflow, fine-tune your models, and power your innovations. 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Keep innovating, stay curious, and we’ll see you next week with more DataPro magic! 🎩✨Take our weekly survey and get a free PDF copy of our best-selling book,"Interactive Data Visualization with Python - Second Edition."We appreciate your input and hope you enjoy the book!Share Your Insights and Shine! 🌟💬Cheers,Merlyn Shelley,Editor-in-Chief, Packt.BOOK TODAY AT $239.99 $399.99JoinGenerativeAI InActionnow withaFull Event Pass for just $239.99—40% off the regular price—with codeFLASH40.Three Reasons Why You Cannot Miss This Event:1. Network with 25+ Leading AI Experts2. Gain Insights from 30+ Dynamic Talks and Hands-On Sessions3. Engage with Experts and Peers through 1:1 Networking, Roundtables, and AMAsAct fast—this FLASH SALE is only for a limited number of seats!CLAIM NOW - LIMITED SEATS📚 Packt Signature Series: Must-Reads & Author Insights➽ RAG-Driven Generative AI: This new title, RAG-Driven Generative AI, is perfect for engineers and database developers looking to build AI systems that give accurate, reliable answers by connecting responses to their source documents. It helps you reduce hallucinations, balance cost and performance, and improve accuracy using real-time feedback and tools like Pinecone and Deep Lake. By the end, you’ll know how to design AI that makes smart decisions based on real-world data—perfect for scaling projects and staying competitive! Start your free trial for access, renewing at $19.99/month.eBook $24.99 $35.99Print + eBook $29.99 $43.99➽ Building Production-Grade Web Applications with Supabase: This new book is all about helping you master Supabase and Next.js to build scalable, secure web apps. It’s perfect for solving tech challenges like real-time data handling, file storage, and enhancing app security. You'll even learn how to automate tasks and work with multi-tenant systems, making your projects more efficient. By the end, you'll be a Supabase pro! Start your free trial for access, renewing at $19.99/month.eBook $15.99 $31.99Print + eBook $27.98 $39.99➽ Python Data Cleaning and Preparation Best Practices: This new book is a great guide for improving data quality and handling. It helps solve common tech issues like messy, incomplete data and missing out on insights from unstructured data. You’ll learn how to clean, validate, and transform both structured and unstructured data—think text, images, and audio—making your data pipelines reliable and your results more meaningful. Perfect for sharpening your data skills! Start your free trial for access, renewing at $19.99/month.eBook $24.99 $35.99Print + eBook $30.99 $44.99🔍 Model Breakdown: Unveiling the Algorithm of the Week➽ Un Ministral, des Ministraux: Mistral AI introduces Ministral 3B and 8B models for edge computing, excelling in knowledge, reasoning, and efficiency. Designed for low-latency, privacy-first use cases, they support up to 128k context length, outperforming competitors while offering compute-efficient solutions for diverse applications.➽ MIBench: A Comprehensive AI Benchmark for Model Inversion Attack and Defense. The postdiscusses Model Inversion (MI) attacks, where attackers attempt to recreate sensitive training data from machine learning models. To address the lack of reliable benchmarks for comparing attacks and defenses, researchers introduced MIBench, a modular toolbox for evaluating MI methods, promoting more consistent, extensible research.➽ OPEN-RAG: A Novel AI Framework Designed to Enhance Reasoning Capabilities in RAG with Open-Source LLMs. This blog discusses Open-RAG, a novel framework designed to improve the reasoning and factual accuracy of retrieval-augmented generation (RAG) models using open-source large language models (LLMs). By transforming LLMs into efficient sparse mixture-of-experts models, Open-RAG excels in handling complex reasoning tasks while balancing accuracy and computational efficiency.➽ Inheritune: An Effective AI Training Approach for Developing Smaller and High-Performing Language Models. This blog discusses Inheritune, a method to train smaller, efficient language models by inheriting early layers from larger pre-trained models and progressively expanding them. Inheritune addresses attention degeneration in deeper layers, achieving performance comparable to larger models with fewer layers.➽ OpenAI’s MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering. This blog introduces MLE-bench, a benchmark created by OpenAI to evaluate AI agents' machine learning engineering skills through 75 Kaggle competitions. The top-performing setup achieved a bronze medal level in 16.9% of competitions, with open-source code available for future research.➽ Update from OpenAI on disrupting deceptive uses of AI: This blog highlights OpenAI's efforts to prevent misuse of its models, particularly during global elections, by disrupting over 20 deceptive networks. It emphasizes ongoing work to enhance AI security and share insights with stakeholders and industry peers.🚀 Trendspotting: What's Next in Tech Trends➽ Announcing BigQuery tables for Apache Iceberg: This blog announces BigQuery tables for Apache Iceberg, a fully managed storage engine offering enterprise-level features like autonomous storage optimization and high-throughput streaming ingestion. It addresses challenges with open-source formats, enabling seamless data management and integration with Apache Spark and Flink.➽ Meet Arch: The Intelligent Layer 7 Gateway for LLM Applications. This blog introduces Arch, an intelligent Layer 7 gateway designed to enhance security, observability, and personalization for large language model (LLM) applications. Arch helps developers efficiently manage sensitive data, track performance, and personalize user interactions in real-time.➽ Researchers from UCLA and Stanford Introduce MRAG-Bench: An AI Benchmark Specifically Designed for Vision-Centric Evaluation for Retrieval-Augmented Multimodal Models. This blog introduces MRAG-Bench, a vision-centric benchmark designed to evaluate large vision-language models (LVLMs) in scenarios where visual knowledge outperforms textual information. It highlights gaps in current models' ability to leverage visual data, encouraging better multimodal understanding.➽ This AI Paper by MIT Introduces Adaptive Computation for Efficient and Cost-Effective Language Models: This blog discusses MIT's innovative approach to improve language model efficiency by adapting computation based on input complexity. Their method dynamically allocates resources, reducing computation by up to 50% without sacrificing performance, optimizing tasks in coding, math, and dialogues.➽ Stanford Researchers Propose LoLCATS: A Cutting Edge AI Method for Efficient LLM Linearization. This blog introduces LoLCATS, a method to efficiently linearize large language models by reducing memory and computational costs without sacrificing quality. Through attention transfer and low-rank adaptation, LoLCATS scales models like Llama 3 70B while maintaining high performance.🛠️ Platform Showdown: Comparing ML Tools & Services➽ 40+ Cool AI Tools You Should Check Out (Oct 2024): This blog highlights various AI tools designed to enhance productivity, creativity, and efficiency across multiple domains, including content creation, personalized media, website building, legal advising, business decision-making, and multimodal capabilities, offering innovative, time-saving solutions.➽ Zyphra Releases Zamba2-7B: A State-of-the-Art Small Language Model. Zyphra's newly released Zamba2-7B is a state-of-the-art small language model that outperforms competitors in quality and speed. Designed for environments with hardware limitations, it combines efficiency, innovative architecture, and open-source availability, democratizing advanced AI.➽ OpenR: An Open-Source AI Framework Enhancing Reasoning in Large Language Models. OpenR is an open-source framework designed to enhance large language models' reasoning abilities through reinforcement learning, process supervision, and advanced inference strategies. It improves reasoning performance in tasks like mathematics and coding, providing a collaborative platform for further advancements.➽ Arcee AI Releases SuperNova-Medius: A 14B Small Language Model Built on the Qwen2.5-14B-Instruct Architecture. SuperNova-Medius, a 14B parameter language model from Arcee AI, balances high performance with accessibility by rivaling larger models like 70B counterparts. It combines innovative optimization techniques for cost-effective, efficient deployment, making advanced AI more inclusive and sustainable.➽ Rhymes AI Released Aria: An Open Multimodal Native MoE Model Offering State-of-the-Art Performance Across Diverse Language, Vision, and Coding Tasks. Aria is an open-source multimodal AI model that integrates text, images, and videos, excelling in complex tasks with its fine-grained mixture-of-experts architecture. It offers competitive performance with lower computational costs, filling a critical gap in accessible multimodal AI.📊 Success Stories: Real-World ML Case Studies➽ NVIDIA AI Researchers Explore Upcycling Large Language Models into Sparse Mixture-of-Experts. Researchers from NVIDIA introduced a method to upcycle pre-trained dense models into Mixture of Experts (MoE) models, enhancing capacity and performance without increasing computational costs. Their technique, using virtual group initialization and softmax-then-topK routing, improved model accuracy and efficiency.➽ Google AI Introduces Tx-LLM: A Large Language Model (LLM) Fine-Tuned fromPaLM-2 to Predict Properties of Many Entities that are Relevant to Therapeutic Development. Tx-LLM, introduced by Google Research and DeepMind, is a fine-tuned large language model designed for diverse therapeutic tasks across drug development. Trained on 709 datasets, it excels in combining molecular and text features, outperforming state-of-the-art models in many tasks.➽ INTELLECT-1: The First Decentralized 10-Billion-Parameter AI Model Training. INTELLECT-1, launched by Prime Intellect AI, is a decentralized initiative to train a 10-billion-parameter AI model, inviting global participation. It challenges centralized AI development, promoting inclusivity, transparency, and collaboration in creating open-source artificial general intelligence (AGI).➽ FPT Software AI Center Introduces HyperAgent: A Groundbreaking Generalist Agent System to Resolve Various Software Engineering Tasks at Scale, Achieving SOTA Performance on SWE-Bench and Defects4J. HyperAgent, introduced by FPT Software AI Center, is a multi-agent system designed to handle a wide range of software engineering tasks. It mimics human developer workflows across phases like planning, code editing, and verification, offering generalizability, efficiency, and scalability.🌍 ML Newsflash: Latest Industry Buzz & Discoveries➽ How to Create Custom Educational Podcasts with NotebookLM? NotebookLM, an AI tool by Google, allows users to create podcasts from documents using two AI voices. These voices discuss the document's key points, making it sound like a real conversation. Users can upload content, customize podcasts, and adjust playback options.➽ How to Create YouTube Video Study Guides with NotebookLM? This blog explains how to use NotebookLM to create study guides from YouTube videos. By uploading video links, NotebookLM generates summaries, FAQs, and structured study materials, making it easier for students and educators to organize key points efficiently.➽ Claude AI: Unboxing Anthropic’s LLM-based AI Assistant, Artifacts & Use Cases. This blog introduces Claude AI, an advanced assistant developed by Anthropic. It highlights Claude's key features, including advanced visual reasoning and "artifacts," which are reusable content pieces that enhance collaborative workflows. Claude excels in business-oriented problem-solving and ethical AI interactions.➽ How to Choose the Best ML Deployment Strategy: Cloud vs. Edge? This blog explores the various methods of deploying machine learning models, emphasizing the differences between cloud and edge deployment. It covers cloud deployment methods like API, serverless, and batch processing, as well as edge deployment for native and web applications, offering pros, cons, and real-world examples.➽ lintsampler: a new way to quickly get random samples from any distribution: lintsampler is a Python package that simplifies and efficiently generates random samples from complex probability distributions. It offers an alternative to traditional methods like MCMC (Markov Chain Monte Carlo), providing an easy, fast, and adaptable approach for sampling across various dimensions and use cases.➽ Learn how to deploy Falcon 2 11B on Amazon EC2 c7i instances for model Inference: This blog introduces the Falcon 2 11B foundation model, developed by Technology Innovation Institute (TII), now deployable on Amazon EC2 c7i instances with Intel AMX support. It explores model quantization (INT8 and INT4) using OpenVINO for efficient, cost-effective real-time AI applications on CPUs.We’ve got more great things coming your way—see you soon!*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{line-height:0;font-size:75%} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}