How-To Tutorials

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!IntroductionLarge Language Models have recently become the talk of the town. I am very sure, you must have heard of ChatGPT. Yes, that’s an LLM, and that’s what I am talking about. Every few weeks, we have been witnessing newer, better but not necessarily larger LLMs coming out either as open-source or closed-source. This is probably the best time to learn about them and make these powerful models work for your specific use case.In today’s blog, we will look into one of the recent open-source models called Llama2 and try to fine-tune it on a standard NLP task of recognizing entities from text. We will first look into what are large language models, what are open-source and closed-source models, and some examples of them. We will then move to learning about Llama2 and why is it so special. We then describe our NLP task and dataset. Finally, we get into coding.About Large Language Models (LLMs)Language models are artificial intelligence systems that have been trained to understand and generate human language. Large Language Models (LLMs) like GPT-3, ChatGPT, GPT-4, Bard, and similar can perform diverse sets of tasks out of the box. Often the quality of output from these large language models is highly dependent on the finesse of the prompt given by the user.These Language models are trained on vast amounts of text data from the Internet. Most of the language models are trained in an auto-regressive way i.e. they try to maximize the probability of the next word based on the words they have produced or seen in the past. This data includes a wide range of written text, from books and articles to websites and social media posts. Language models have a wide range of applications, including chatbots, virtual assistants, content generation, and more. They can be used in industries like customer service, healthcare, finance, and marketing.Since these models are trained on enormous data, they are already good at zero-shot inference and can be steered to perform better with few-shot examples. Zero-shot is a setup in which a model can learn to recognize things that it hasn't explicitly seen before in training. In a Few-shot setting, the goal is to make predictions for new classes based on the few examples of labeled data that is provided to it at inference time.Despite their amazing capabilities of generating text, these humongous models come with a few limitations that must be thought of when building an LLM-based production pipeline. Some of these limitations are hallucinations, biases, and more.Closed and Open-source Language ModelsLarge language models from closed-source are those employed by some companies and are not readily accessible to the public. Training data for these models are typically kept private. While they can be highly sophisticated, this limits transparency, potentially leading to concerns about bias, and data privacy.In contrast, open-source projects like GPT-3, are designed to be freely available to researchers and developers. These models are trained on extensive, publicly available datasets, allowing for a degree of transparency and collaboration.The decision between closed- and open-source language models is influenced by several variables, such as the project's goals, the need for openness, and others.About LLama2Meta's open-source LLM is called Llama 2. It was trained with 2 trillion "tokens" from publicly available sources like Wikipedia, Common Crawl, and books from the Gutenberg project. Three different parameter level model versions are available, i.e. 7 billion, 13 billion, and 70 billion parameter models. There are two types of completion models available: Chat-tuned and General. The chat-tuned models that have been fine-tuned for chatbot-like dialogue are denoted by the suffix '-chat'. We will use general Meta's 7b Llama-2 huggingface model as the base model that we fine-tune. Feel free to use any other version of llama2-7b.Also, if you are interested, there are several threads that you can go through to understand how good is Llama family w.r.t GPT family is - source, source, source.About Named Entity RecognitionAs a component of information extraction, named-entity recognition locates and categorizes specific entities inside the unstructured text by allocating them to pre-defined groups, such as individuals, organizations, locations, measures, and more. NER offers a quick way to understand the core idea or content of a lengthy text.There are many ways of extracting entities from a given text, in this blog, we will specifically delve into fine-tuning Llama2-7b using PEFT techniques on Colab Notebook.We will transform the SMSSpamCollection classification data set for NER. Pretty interesting 😀We search through all 10 letter words and tag them as 10_WORDS_LONG. And this is the entity that we want our Llama to extract. But why this bizarre formulation? I did it intentionally to show that this is something that the pre-trained model would not have seen during the pre-training stage. So it becomes essential to fine-tune it and make it work for our use case 👍. But surely we can add logic to our formulation - think of these words as probable outliers/noisy words. The larger the words, the higher the possibility of it being noise/oov. However, you will have to come up with the extract letter count after seeing the word length distribution. Please note that the code is generic enough for fine-tuning any number of entities. It’s just a change in the data preparation step that we will make to slice out only relevant entities.Code for Fine-tuning Llama2-7b# Importing Libraries from transformers import LlamaTokenizer, LlamaForCausalLM import torch from datasets import Dataset import transformers import pandas as pd from peft import get_peft_model, LoraConfig, TaskType, prepare_model_for_int8_training, get_peft_model_state_dict, PeftModel from sklearn.utils import shuffleData Preparation Phasedf = pd.read_csv('SMSSpamCollection', sep='\t', header=None)  all_text = df[1].str.lower().tolist()  input, output = [], []  for text in all_text:               input.append(text)               output.append({word: '10_WORDS_LONG' for word in text.split() if len(word)==10}) df = pd.DataFrame([input, output]).T df.rename({0:'input_text', 1: 'output_text'}, axis=1, inplace=True) print (df.head(5)) total_ds = shuffle(df, random_state=42) total_train_ds = total_ds.head(4000) total_test_ds = total_ds.tail(1500) total_train_ds_hf = Dataset.from_pandas(total_train_ds) total_test_ds_hf = Dataset.from_pandas(total_test_ds) tokenized_tr_ds = total_train_ds_hf.map(generate_and_tokenize_prompt) tokenized_te_ds = total_test_ds_hf.map(generate_and_tokenize_prompt) Fine-tuning Phase# Loading Modelmodel_name = "meta-llama/Llama-2-7b-hf" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name) def create_peft_config(m): peft_cofig = LoraConfig( task_type=TaskType.CAUSAL_LM, inference_mode=False, r=8, lora_alpha=16, lora_dropout=0.05, target_modules=['q_proj', 'v_proj'], ) model = prepare_model_for_int8_training(model) model.enable_input_require_grads() model = get_peft_model(model, peft_cofig) model.print_trainable_parameters() return model, peft_cofig model, lora_config = create_peft_config(model) def generate_prompt(data_point): return f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. ### Instruction: Extract entity from the given input: ### Input: {data_point["input_text"]} ### Response: {data_point["output_text"]}""" tokenizer.pad_token_id = 0 def tokenize(prompt, add_eos_token=True): result = tokenizer( prompt, truncation=True, max_length=128, padding=False, return_tensors=None, ) if ( result["input_ids"][-1] != tokenizer.eos_token_id and len(result["input_ids"]) < 128 and add_eos_token ): result["input_ids"].append(tokenizer.eos_token_id) result["attention_mask"].append(1) result["labels"] = result["input_ids"].copy() return result def generate_and_tokenize_prompt(data_point): full_prompt = generate_prompt(data_point) tokenized_full_prompt = tokenize(full_prompt) return tokenized_full_prompt training_arguments = transformers.TrainingArguments(  per_device_train_batch_size=1, gradient_accumulation_steps=16,  learning_rate=4e-05,  logging_steps=100,  optim="adamw_torch",  evaluation_strategy="steps",  save_strategy="steps",  eval_steps=100,  save_steps=100,  output_dir="saved_models/" ) data_collator = transformers.DataCollatorForSeq2Seq(tokenizer) trainer = transformers.Trainer(model=model, tokenizer=tokenizer, train_dataset=tokenized_tr_ds, eval_dataset=tokenized_te_ds, args=training_arguments, data_collator=data_collator) with torch.autocast("cuda"):       trainer.train()InferenceLoaded_tokenizer = LlamaTokenizer.from_pretrained(model_name) Loaded_model = LlamaForCausalLM.from_pretrained(model_name, load_in_8bit=True, torch.dtype=torch.float16, device_map=’auto’) Model = PeftModel.from_pretrained(Loaded_model, “saved_model_path”, torch.dtype=torch.float16) Model.config.pad_tokeni_id = loaded_tokenizer.pad_token_id = 0 Model.eval() def extract_entity(text):   inp = Loaded_tokenizer(prompt, return_tensor=’pt’).to(“cuda”)   with torch.no_grad():       P_ent = Loaded_tokenizer.decode(model.generate(**inp, max_new_tokens=128)[0], skip_special_tokens=True)       int_idx = P_ent.find(‘Response:’)       P_ent = P_ent[int_idx+len(‘Response:’):]   return P_ent.strip() extracted_entity = extract_entity(text) print (extracted_entity) ConclusionWe covered the process of optimizing the llama2-7b model for the Named Entity Recognition job in this blog post. For that matter, it can be any task that you are interested in. The core concept that one must learn from this blog is PEFT-based training of large language models. Additionally, as pre-trained LLMs might not always perform well in your work, it is best to fine-tune these models.Author BioPrakhar Mishra has a Master’s in Data Science with over 4 years of experience in industry across various sectors like Retail, Healthcare, Consumer Analytics, etc. His research interests include Natural Language Understanding and generation, and has published multiple research papers in reputed international publications in the relevant domain. Feel free to reach out to him on LinkedIn

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!IntroductionBusinesses thrive on information in today's data-driven era. However, raw data often needs enrichment to reveal its full potential. Here enters ChatGPT, a powerful tool not only for communication but also for enhancing data enrichment processes.Let us delve into the prospects of using ChatGPT for data enrichment.Does ChatGPT Do Data Mining?ChatGPT's prowess extends to data mining, unraveling valuable insights from vast datasets. Its natural language processing abilities allow it to decipher complex data structures, making it a versatile ally for researchers and analysts. By processing textual data, ChatGPT identifies patterns, enabling efficient data mining techniques.Process of data mining by ChatGPTChatGPT's ability to assist in data mining stems from its advanced natural language processing (NLP) capabilities. Here's an elaboration on the process of how ChatGPT can be utilized for data mining:1. Understanding Natural Language Queries:ChatGPT excels at understanding complex natural language queries. When provided with a textual prompt, it comprehends the context and intent behind the query. This understanding forms the basis for its data mining capabilities.2. Processing and Analyzing Textual Data:ChatGPT can process large volumes of textual data, including articles, reports, customer reviews, social media posts, etc. It can identify patterns, extract relevant information, and summarize lengthy texts, making it valuable for extracting insights from textual data sources.3. Contextual Analysis:ChatGPT performs contextual analysis to understand the relationships between words and phrases in a text. This contextual understanding enables ChatGPT to identify entities (such as names, places, and products) and their connections within the data, enhancing the precision of data mining results.4. Topic Modeling:ChatGPT can identify prevalent topics within textual data. Recognizing recurring themes and keywords helps categorize and organize large datasets into meaningful topics. This process is essential for businesses seeking to understand trends and customer preferences from textual data sources.5. Sentiment Analysis:ChatGPT can assess the sentiment expressed in textual data, distinguishing between positive, negative, and neutral sentiments. Sentiment analysis is crucial for businesses to gauge customer satisfaction, brand perception, market sentiment from online posts and reviews, and customer feedback.6. Data Summarization:ChatGPT can summarize extensive datasets, condensing large volumes of information into concise and informative summaries. This summarization capability is valuable for data mining, enabling analysts to quickly grasp essential insights without delving into voluminous data sources.7. Custom Queries and Data Extraction:Users can formulate custom queries and prompts tailored to specific data mining tasks. By asking ChatGPT precise questions about the data, users can extract targeted information, enabling them to focus on the particular aspects of the data relevant to their analysis.8. Interactive Exploration:ChatGPT allows for interactive exploration of data. Users can iteratively refine their queries based on the responses received, enabling a dynamic and exploratory approach to data mining. This interactivity facilitates a deeper understanding of the data and helps uncover hidden patterns and insights.By leveraging these capabilities, ChatGPT assists in data mining by transforming unstructured textual data into structured, actionable insights. Its adaptability to various queries and ability to process and analyze large datasets make it a valuable tool for businesses and researchers engaged in data mining.ChatGPT's ability to analyze JSON dataChatGPT can seamlessly analyze JSON data, a fundamental format for structuring data. Leveraging Python, integrating ChatGPT with JSON data becomes straightforward. Below is an illustrative Python code snippet demonstrating this integration:import openai import json # Your JSON data json_data = {    "key": "value",    "array": [1, 2, 3],    "nested": {        "inner_key": "inner_value"    } } # Convert JSON data to a string json_string = json.dumps(json_data) # Interact with ChatGPT response = openai.Completion.create(    engine="text-davinci-003",    prompt=json_string,    max_tokens=150  # Adjust the token limit as needed ) print(response.choices[0].text.strip())Also, ChatGPT can be used to analyze JSON data in various ways. Here are a few more examples of how ChatGPT can assist with JSON data analysis:1. JSON Data SummarizationChatGPT can be prompted to summarize complex JSON data structures, highlighting key attributes and relationships. For instance:Prompt: "Can you summarize the following JSON data for me?" {    "user": {        "name": "Sbani Paul",        "age": 30,        "email": "sbanipaul@example.com"    },    "order": {        "id": "123456",        "products": ["Widget A", "Widget B", "Widget C"],        "total": 150.75    } }ChatGPT Response: "The JSON data consists of user information and an order summary. The user, Sbani Paul, is 30 years old and can be reached at sbanipaul@example.com. The order with ID 123456 includes products Widget A, Widget B, and Widget C, totaling $150.75."2. JSON Schema ValidationChatGPT can help validate JSON data against a specified schema, ensuring data integrity and adherence to predefined data structures. For example:Prompt: "Is this JSON data valid according to the specified schema?" {    "name": "Aliza",    "age": 25,    "email": "Aliza@example.com" }ChatGPT Response: "Yes, the provided JSON data is valid according to the specified schema. It includes 'name' (string), 'age' (number), and 'email' (string) fields."3. JSON Data Transformation ChatGPT can assist in transforming JSON data into different formats, for instance, converting JSON to CSV or XML formats for specific use cases or applications.Prompt: "How can I transform this JSON data into CSV format?" [    {        "name": "Aliza",        "age": 30,        "city": "New York"    },    {        "name": "Bob",        "age": 35,        "city": "Los Angeles"    } ] ChatGPT Response: "To transform the given JSON data into CSV format, you can represent each object as a row and use commas to separate values. Here's the CSV representation:name,age,cityAliza,30,New YorkBob,35,Los Angeles4. Nested JSON Data AnalysisChatGPT can navigate through nested JSON structures, providing insights into specific elements within the data hierarchy.Prompt: "Can you provide details about the 'address' field in this nested JSON data?" {    "user": {        "name": "Eva",        "age": 28,        "address": {            "street": "123 Main St",            "city": "Anytown",            "postal_code": "54321"        }    } }ChatGPT Response: "Certainly, the 'address' field contains specific details. The user, Eva, resides at 123 Main St in Anytown with the postal code 54321."ChatGPT's ability to comprehend and respond to prompts about JSON data makes it a valuable tool for developers and data analysts working with structured data formats. Whether it's validation, transformation, or detailed analysis, ChatGPT can assist in various aspects of JSON data processing.What Is the Data Enrichment Method?Data enrichment transforms raw data into a goldmine of insights. This process involves augmenting existing data with supplementary information. Techniques include:Web scraping for real-time dataAPI integrations for seamless access to external databases.Leveraging machine learning algorithms to predict missing data.Data enrichment amplifies the value of datasets, enhancing analytical depth. The methods are diverse and dynamic, tailored to enhance the value of raw data. Let us go through an elaboration on the fundamental techniques of data enrichment:1. Web ScrapingWeb scraping involves extracting data from websites. It enables businesses to gather real-time information, news updates, pricing details, and more. By scraping relevant websites, organizations enrich their datasets with the latest and most accurate data available on the web. Web scraping tools can be programmed to extract specific data points from various web pages, ensuring the enrichment of databases with up-to-date information.2. API IntegrationsApplication Programming Interfaces (APIs) act as bridges between different software systems. Many platforms provide APIs that allow seamless data exchange. By integrating APIs into data enrichment processes, businesses can access external databases, social media platforms, weather services, financial data, and other sources. This integration ensures that datasets are augmented with comprehensive and diverse information, enhancing their depth and relevance.3. ChatGPT InteractionChatGPT's natural language processing abilities make it a valuable tool for data enrichment. Businesses can interact with ChatGPT to extract context-specific information by providing specific prompts. For example, ChatGPT can be prompted to summarize lengthy textual documents, analyze market trends, or provide detailed explanations about particular topics. These interactions enrich datasets by incorporating expert insights and detailed analyses, enhancing the overall understanding of the data.4. Machine Learning AlgorithmsMachine learning algorithms are pivotal in data enrichment, especially when dealing with large datasets. These algorithms can predict missing data points by analyzing patterns within the existing dataset. A variety of strategies, such as regression analysis, decision trees, and neural networks, are employed to fill gaps in the data intelligently. By accurately predicting missing values, machine learning algorithms ensure that datasets are complete and reliable, making them suitable for in-depth analysis and decision-making.5. Data Normalization and TransformationData normalization involves organizing and structuring data in a consistent format. It ensures that data from disparate sources can be effectively integrated and compared. Conversely, transformation consists of converting data into a standardized format, making it uniform and compatible. These processes are crucial for data integration and enrichment, enabling businesses to use consistent, high-quality data.6. Data AugmentationData augmentation involves expanding the dataset by creating variations of existing data points. In machine learning, data augmentation techniques are often used to enhance the diversity of training datasets, leading to more robust models. By applying similar principles, businesses can create augmented datasets for analysis, providing a broader perspective and enhancing the accuracy of predictions and insights.By employing these diverse methods, businesses can ensure their datasets are comprehensive and highly valuable. Data enrichment transforms raw data into a strategic asset, empowering organizations to make data-driven decisions to gain a competitive edge in their respective industries.ConclusionIncorporating ChatGPT into data enrichment workflows revolutionizes how businesses harness information. By seamlessly integrating with various data formats and employing diverse enrichment techniques, ChatGPT ensures that data isn't just raw facts but a source of actionable intelligence. Stay ahead in the data game – leverage ChatGPT to unlock the full potential of your datasets.Author BioJyoti Pathak is a distinguished data analytics leader with a 15-year track record of driving digital innovation and substantial business growth. Her expertise lies in modernizing data systems, launching data platforms, and enhancing digital commerce through analytics. Celebrated with the "Data and Analytics Professional of the Year" award and named a Snowflake Data Superhero, she excels in creating data-driven organizational cultures.Her leadership extends to developing strong, diverse teams and strategically managing vendor relationships to boost profitability and expansion. Jyoti's work is characterized by a commitment to inclusivity and the strategic use of data to inform business decisions and drive progress.

👋 Hello ,Welcome to another captivating edition of AI_Distilled, featuring recent advancements in training and fine-tuning LLMs, GPT and AI models for enhanced business outcomes.Let’s begin our news and analysis with an industry expert’s opinion.  “Artificial intelligence is the science of making machines do things that would require intelligence if done by humans” – John McCarthy, Computer Scientist and AI Visionary. AI does indeed make machines intelligent, so much so that industry titans are now waging a proxy AI war with billions in startup funding. Without a doubt, AI is onto something big! In this week, we’ll talk about Biden's AI Executive Order, which has been praised for scope but deemed insufficient without legislation, Perplexity's AI Search Engine, OpenAI launching new team and challenge to prepare for catastrophic risks of advanced AI, Google Invests $2 Billion in Anthropic, and updating its Bug Bounty program to address AI security concerns. Look out for your fresh dose of AI resources, secret knowledge, and tutorials on how to use custom AI models to enhance complex technical workflows, improving LLM understanding with user feedback, and essential text preprocessing for effective machine learning with Python. 📥 Feedback on the Weekly EditionWhat do you think of this issue and our newsletter?Please consider taking the short survey below to share your thoughts and you will get a free PDF of the “The Applied Artificial Intelligence Workshop” eBook upon completion. Complete the Survey. Get a Packt eBook for Free!Writer’s Credit: Special shout-out to Vidhu Jain for their valuable contribution to this week’s newsletter content!  Cheers,  Merlyn Shelley  Editor-in-Chief, Packt  SignUp | Advertise | Archives⚡ TechWave: AI/GPT News & Analysis🔹 OpenAI Launches New Team and Challenge to Prepare for Catastrophic Risks of Advanced AI: The ChatGPT creator announced new efforts to prepare for potential catastrophic risks associated with highly advanced AI systems. The company is forming a new internal team called "Preparedness" to assess risks ranging from cybersecurity threats to autonomous biological replication. It is also launching an "AI Preparedness Challenge" with prize money to crowdsource ideas for preventing misuse of advanced AI. OpenAI says it aims to benefit humanity with cutting-edge AI while taking seriously the full spectrum of safety risks.🔹 Biden's AI Executive Order Praised for Scope but Deemed Insufficient Without Legislation: President Biden recently issued an executive order on AI that experts say covers important ground but lacks teeth without accompanying legislation from Congress. The order establishes guidelines and oversight for AI development and use, including in healthcare. However, many provisions simply codify voluntary industry practices. Stakeholders say Congress must pass more comprehensive AI regulations, but partisan disputes make near-term action unlikely.  🔹 Google Updates Bug Bounty Program to Address AI Security Concerns: Google has expanded its vulnerability rewards program to include incentives for discovering potential abuses of artificial intelligence systems. The update comes as worries grow over generative AI being exploited maliciously. Under the revised guidelines, security researchers can earn financial rewards for uncovering AI training data extraction that leaks private information. The move aligns with AI companies' recent White House pledge to better identify AI vulnerabilities.  🔹 Perplexity's AI Search Engine Garners $500M Valuation After New Funding: The AI startup Perplexity recently secured additional funding led by venture capital firm IVP, garnering a $500 million valuation. Perplexity is developing a conversational search engine to challenge Google's dominance using artificial intelligence. The company's iOS app and website traffic have been growing steadily amid rising interest in AI like ChatGPT. With deep ties to Google researchers, Perplexity leverages LLMs and has attracted investments from major industry figures.  🔹 Tech Giants Wage Proxy AI War with Billions in Startup Funding As Google Invests $2 Billion in Anthropic: Major technology companies like Google, Microsoft, and Amazon are investing billions in AI startups like OpenAI and Anthropic as surrogates in the race to lead the AI space. Unable to quickly build their own capabilities in large language models, the tech giants are funneling massive sums into the AI leaders to gain ownership stakes and technology access. Anthropic's $2 billion funding from Google follows similar multibillion investments from Microsoft and Amazon, fueling an expensive AI innovation war by proxy.  🔹 Poe Unveils Monetization for Third-Party Conversational AI Developers: The AI chatbot platform Poe has introduced a new revenue sharing model to let creators’ profit from building specialized bots. Poe will split subscription fees and pay per-message charges to offset infrastructure costs. An open API also allows adding custom natural language models beyond Poe's defaults. The moves aim to spur innovation by empowering niche developers. Poe believes reducing barriers will increase diversity, not just competition.   🔮 Expert Insights from Packt Community Generative AI with Python and TensorFlow 2 - By Joseph Babcock , Raghav Bali  Kubeflow: an end-to-end machine learning lab As was described at the beginning of this chapter, there are many components of an end-to-end lab for machine learning research and development (Table 2.1), such as: A way to manage and version library dependencies, such as TensorFlow, and package them for a reproducible computing environment Interactive research environments where we can visualize data and experiment with different settings A systematic way to specify the steps of a pipeline – data processing, model tuning, evaluation, and deployment Provisioning of resources to run the modeling process in a distributed manner Robust mechanisms for snapshotting historical versions of the research process As we described earlier in this chapter, TensorFlow was designed to utilize distributed resources for training. To leverage this capability, we will use the Kubeflow projects. Built on top of Kubernetes, Kubeflow has several components that are useful in the end-to-end process of managing machine learning applications. Using Kubeflow Katib to optimize model hyperparameters Katib is a framework for running multiple instances of the same job with differing inputs, such as in neural architecture search (for determining the right number and size of layers in a neural network) and hyperparameter search (finding the right learning rate, for example, for an algorithm). Like the other Customize templates we have seen, the TensorFlow job specifies a generic TensorFlow job, with placeholders for the parameters: apiVersion: "kubeflow.org/v1alpha3" kind: Experiment metadata:  namespace: kubeflow  name: tfjob-example spec: parallelTrialCount: 3  maxTrialCount: 12  maxFailedTrialCount: 3  objective:    type: maximize    goal: 0.99    objectiveMetricName: accuracy_1  algorithm:    algorithmName: random  metricsCollectorSpec:    source:      fileSystemPath:        path: /train        kind: Directory    collector:      kind: TensorFlowEvent  parameters:    - name: --learning_rate      parameterType: double      feasibleSpace:        min: "0.01"        max: "0.05"    - name: --batch_size      parameterType: int      feasibleSpace:        min: "100"        max: "200"  trialTemplate:    goTemplate:        rawTemplate: |-          apiVersion: "kubeflow.org/v1"          kind: TFJob          metadata:            name: {{.Trial}}            namespace: {{.NameSpace}}          spec:           tfReplicaSpecs:            Worker:              replicas: 1               restartPolicy: OnFailure              template:                spec:                  containers:                    - name: tensorflow                       image: gcr.io/kubeflow-ci/tf-mnist-with-                             summaries:1.0                      imagePullPolicy: Always                      command:                        - "python"                        - "/var/tf_mnist/mnist_with_summaries.py"                        - "--log_dir=/train/metrics"                        {{- with .HyperParameters}}                        {{- range .}}                        - "{{.Name}}={{.Value}}"                        {{- end}}                        {{- end}}  which we can run using the familiar kubectl syntax: kubectl apply -fhttps://raw.githubusercontent.com/kubeflow/katib/master/examples/v1alpha3/tfjob-example.yaml This content is from the book “Generative AI with Python and TensorFlow 2” by Joseph Babcock , Raghav Bali (April 2021). Start reading a free chapter or access the entire Packt digital library free for 7 days by signing up now. To learn more, click on the button below. Read through the Chapter 1 unlocked here...  🌟 Secret Knowledge: AI/LLM Resources🔹 How to Use Custom AI Models to Enhance Complex Technical Workflows: In this post, you'll learn how Nvidia’s researchers leveraged customized LLMs to streamline intricate semiconductor chip design. The research demonstrates how to refine foundation models into customized assistants that understand industry-specific patterns. You'll see how careful data cleaning and selection enables high performance even with fewer parameters. The post explores step-by-step instructions on how researchers built a specialized AI that helps with writing code, improving documentation, and optimizing complex technical workflows.  🔹 How to Build Impactful LLM Applications: In this post, you'll explore lessons learned from creating Microsoft's Copilot products, such as Viva and PowerPoint. It discusses how combining LLMs with app context and other ML models can be a game-changer and demonstrates how parsing user queries and responses enables precise skill activation. By following their approach of utilizing multiple models to summarize insights without losing nuance, you can gain practical tips for your own LLM application development. 🔹 Understanding Convolutional Neural Networks and Vision Transformers: A Mathematical Perspective: You'll learn about convolutional neural networks and vision transformers in this post. They're great for image classification but differ in math, especially for generative tasks. You'll see how their training budgets work and understand their unique math. We'll also discuss their differences in complexity and memory usage. Plus, you'll learn why convolutional nets handle spatial coherence naturally, while vision transformers might need some help. By the end, you'll know why transformers are better for generating sequential data.  🔹 Improving Large Language Model Understanding with User Feedback: The post focuses on improving user intent detection for LLMs by utilizing disambiguation, context, and MemPrompt. These techniques enhance LLM responses, enabling better understanding of user intent, offering real-time feedback, and enhancing LLM performance and utility. 🔹 The Power of High-Quality Data in Language Models: The article emphasizes the significance of high-quality data for Large Language Models (LLMs). It introduces the concept of alignment, discussing how it influences LLM behavior. The article stresses the vital role of data quality and diversity in optimizing LLM performance and capabilities.  💡 Masterclass: AI/LLM Tutorials🔹 Enhance Language Model Performance with Step-Back Prompting: This guide explores the use of Step-Back Prompting to enhance LLMs' performance in complex tasks, like knowledge-intensive QA and multi-hop reasoning. It offers a step-by-step tutorial, including package setup and data collection, to implement this approach, potentially improving AI model behavior and responses.  🔹 Boosting AI at Scale with Vectorized Databases: This guide explores how vectorized databases are transforming LLMs like GPT-3 by enhancing their capabilities and scalability. It explains the principles of LLMs and the role of vectorized databases in empowering them. It discusses efficient data retrieval, optimization of vector operations, and scaling for real-time responses. The guide highlights use cases, including content generation and recommendation systems, where vectorized databases excel, and addresses the challenges of adopting them for LLMs. 🔹 Mastering Data Mining with GPT-4: A Practical Guide Using Seattle Weather Data: This guide explores the use of GPT-4 for data mining using Seattle's weather dataset. It covers AI's potential in data mining, detailing the process from exploratory data analysis to clustering and anomaly detection. GPT-4 assists in data loading, EDA, data cleaning, feature engineering, and suggests clustering methods. The post highlights the collaborative aspect of AI-human interaction and how GPT-4 can improve data mining and data analysis in the field of data science. 🔹 Introduction to Reinforcement Learning and AWS Deepracer: This post introduces reinforcement learning, a machine learning approach focused on maximizing rewards through agent-environment interactions. It compares it to motivating students based on performance. It explores practical applications via AWS Deepracer for self-driving cars, explaining key components and mentioning the Deepracer Student League as a learning opportunity.  🔹 Essential Text Preprocessing for Effective Machine Learning with Python: This post highlights crucial text preprocessing techniques for machine learning. It emphasizes the need to clean text data to avoid interference and unintended word distinctions. The methods, including removing numbers and handling extra spaces, enhance text data quality for effective machine learning applications.  🚀 HackHub: Trending AI Tools🔹 Pythagora-io/gpt-pilot: Boosts app development speed 20x via requirement specification, oversight, and coding assistance through clarifications and reviews. 🔹 hkuds/rlmrec: PyTorch implementation for the RLMRec model, enhancing recommenders with LLMs for advanced representation learning in recommendation systems. 🔹 THUDM/AgentTuning: Empowers LLMs by instruction-tuning them with interaction trajectories from various agent tasks, enhancing their generalization and language abilities. 🔹 cpacker/MemGPT: Enhances LLMs by intelligently managing memory tiers, enabling extended context and perpetual conversations.

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!IntroductionHello there, fellow explorer! So, you've been hearing about this thing called 'quantum computing' and how it promises to revolutionize... well, almost everything. And you're curious about how we can harness its power, right? But there's a twist: you want to use ChatGPT to help guide the process. Intriguing! In this tutorial, I'll take you by the hand, and together, we'll craft some amazing use cases for quantum computing, all with the help of ChatGPT prompts.First, we'll lay down our goals. What exactly do we want to achieve with quantum computing? Maybe it's predicting the weather years in advance, or understanding the deep mysteries of our oceans. Once we have our roadmap, it's time to gather our tools and data. Here's where satellites, weather stations, and another cool tech come in.But data can be messy, right? No worries! We'll clean it up and get it ready for our quantum adventure. And then, brace yourself, because we're diving deep into the world of quantum mechanics. But fear not! With ChatGPT by our side, we'll decode the jargon and make it all crystal clear.The next steps? Designing our very own quantum algorithms and giving them a test run. It's like crafting a recipe and then baking the perfect cake. Once our quantum masterpiece is ready, we'll look at the results, decipher what they mean, and integrate them with existing tools. And because we always strive for perfection, we'll continuously refine our approach, ensuring it's the best it can be.Here's a streamlined 10-step process for modeling complex climate systems using quantum computing:Step 1. Objective Definition: Clearly define the specific goals of climate modeling, such as predicting long-term temperature changes, understanding oceanic interactions, or simulating atmospheric phenomena.Step 2. Data Acquisition: Gather comprehensive climate data from satellites, ground stations, and other relevant sources, focusing on parameters crucial for the modeling objectives.Step 3. Data Preprocessing: Clean and transform the climate data into a format suitable for quantum processing, addressing any missing values, inconsistencies, or noise.Step 4. Understanding Quantum Mechanics: Familiarize with the principles and capabilities of quantum computing, especially as they relate to complex system modeling.Step 5. Algorithm Selection/Design: Choose or develop quantum algorithms tailored to model the specific climate phenomena of interest. Consider hybrid algorithms that leverage both classical and quantum computations.Step 6. Quantum Simulation: Before deploying on real quantum hardware, simulate the chosen quantum algorithms on classical systems to gauge their efficacy and refine them as needed.Step 7. Quantum Execution: Implement the algorithms on quantum computers, monitoring performance and ensuring accurate modeling of the climate system.Step 8. Result Interpretation: Analyze the quantum computing outputs, translating them into actionable climate models, predictions, or insights.Step 9. Integration & Application: Merge the quantum-enhanced models with existing climate research tools and methodologies, ensuring the findings are accessible and actionable for researchers, policymakers, and stakeholders.Step 10. Review & Iteration: Regularly evaluate the quantum modeling process, updating algorithms and methodologies based on new data, quantum advancements, or evolving climate modeling objectives.Using quantum computing for modeling complex climate systems holds promise for more accurate and faster simulations, but it's essential to ensure the approach is methodical and scientifically rigorous.So, are you ready to create some quantum magic with ChatGPT? Let's jump right in!1. Objective DefinitionPrompt: "ChatGPT, can you help me outline the primary objectives and goals when modeling complex climate systems? What are the key phenomena and parameters we should focus on?"2. Data AcquisitionPrompt:"ChatGPT, where can I source comprehensive climate data suitable for quantum modeling? Can you list satellite databases, ground station networks, or other data repositories that might be relevant?"3. Data PreprocessingPrompt:"ChatGPT, what are the best practices for preprocessing climate data for quantum computing? How do I handle missing values, inconsistencies, or noise in the dataset?"4. Understanding Quantum MechanicsPrompt:"ChatGPT, can you give me a primer on the principles of quantum computing, especially as they might apply to modeling complex systems like climate?"5. Algorithm Selection/DesignPrompt:"ChatGPT, what quantum algorithms or techniques are best suited for climate modeling? Are there hybrid algorithms that combine classical and quantum methods for this purpose?"6. Quantum SimulationPrompt:"ChatGPT, how can I simulate quantum algorithms on classical systems before deploying them on quantum hardware? What tools or platforms would you recommend?"7. Quantum Execution Prompt:"ChatGPT, what are the steps to implement my chosen quantum algorithms on actual quantum computers? Are there specific quantum platforms or providers you'd recommend for climate modeling tasks?"8. Result InterpretationPrompt:"ChatGPT, once I have the outputs from the quantum computation, how do I interpret and translate them into meaningful climate models or predictions?"9. Integration & ApplicationPrompt:"ChatGPT, how can I integrate quantum-enhanced climate models with existing research tools and methodologies? What steps should I follow to make these models actionable for the broader research community?"10. Review & IterationPrompt:"ChatGPT, how should I periodically evaluate and refine my quantum modeling approach? What metrics or feedback mechanisms can help ensure the process remains optimal and up-to-date?"These prompts are designed to guide a user in leveraging ChatGPT's knowledge and insights for each step of the quantum computing-based climate modeling process.ConclusionAnd there you have it! From setting clear goals to diving into the intricate world of quantum mechanics and finally crafting our very own quantum algorithms, we've journeyed through the fascinating realm of quantum computing together. With ChatGPT as our trusty guide, we've unraveled complex concepts, tackled messy data, and brewed some quantum magic. It's been quite the adventure, hasn't it? Remember, the world of quantum computing is vast and ever-evolving, so there's always more to explore and learn. Whether you're a seasoned quantum enthusiast or just starting out, I hope this guide has ignited a spark of curiosity in you. As we part ways on this tutorial journey, I encourage you to keep exploring, questioning, and innovating. The quantum realm awaits your next adventure. Until next time, happy quantum-ing!Author BioDr. Anshul Saxena is an author, corporate consultant, inventor, and educator who assists clients in finding financial solutions using quantum computing and generative AI. He has filed over three Indian patents and has been granted an Australian Innovation Patent. Anshul is the author of two best-selling books in the realm of HR Analytics and Quantum Computing (Packt Publications). He has been instrumental in setting up new-age specializations like decision sciences and business analytics in multiple business schools across India. Currently, he is working as Assistant Professor and Coordinator – Center for Emerging Business Technologies at CHRIST (Deemed to be University), Pune Lavasa Campus. Dr. Anshul has also worked with reputed companies like IBM as a curriculum designer and trainer and has been instrumental in training 1000+ academicians and working professionals from universities and corporate houses like UPES, CRMIT, and NITTE Mangalore, Vishwakarma University, Pune & Kaziranga University, and KPMG, IBM, Altran, TCS, Metro CASH & Carry, HPCL & IOC. With a work experience of 5 years in the domain of financial risk analytics with TCS and Northern Trust, Dr. Anshul has guided master's students in creating projects on emerging business technologies, which have resulted in 8+ Scopus-indexed papers. Dr. Anshul holds a PhD in Applied AI (Management), an MBA in Finance, and a BSc in Chemistry. He possesses multiple certificates in the field of Generative AI and Quantum Computing from organizations like SAS, IBM, IISC, Harvard, and BIMTECH.Author of the book: Financial Modeling Using Quantum Computing

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!IntroductionContent curation is a crucial aspect of giving your audience the right information, in an appropriate and timely manner. This involves selecting, organising and displaying content from a variety of sources. You can enhance your content curation process through ChatGPT, which is based on the advanced language model of OpenAI.In this hands-on guide, you'll learn how to use the ChatGPT for intelligent content curation, with step-by-step examples and accompanied by expected output.Why Intelligent Content Curation?Intelligent content curation is important for a number of reasons, it is important that content be curated in an efficient manner. In particular, it may provide you with a means of saving time and resources. You will be able to focus on other tasks such as developing new content or interacting with your viewers by using an automation of the content curation process.Second, you may be able to enhance the quality of your content with Intelligent Content Curation. From a range of sources, such as academia, industry publications, and social media, ChatGPT is able to identify appropriate content. It means you can be certain that your content is based on the most recent information and research.Lastly, you might benefit from intelligent content curation in reaching out to a wider audience. You can take advantage of the ChatGPT service to determine which content is most relevant for your target audience and how it should be spread across a number of channels. That way, you'll be able to increase traffic on your website, engage and your social media following.How ChatGPT Can Be Used for Intelligent Content Curation?ChatGPT is a sophisticated AI language model that can be used to perform any number of functions, e.g. in the intelligent curation of articles. The following may be used with ChatGPT:Generate search queries: ChatGPT can be used to generate search queries that are relevant to a specific topic and audience. This can be done by providing ChatGPT with a brief description of the topic and the audience.Identify relevant content: The identification of relevant content from a range of sources can be carried out using ChatGPT. If this is the case, you may provide your ChatGPT with a list of URLs or access to an online content database.Select the best content: In the case of selecting content, ChatGPT will be able to organize it into an understandable and interactive format. To do that, you can send a template to ChatGPT or give instructions on how the content should be organized.Organize the content: To organize the collected content in a logical and interesting format, it is possible to use ChatGPT. This is possible by providing ChatGPT with a template, which will give instructions on how to organize the content.Prerequisites and Setting Up the EnvironmentLet's provide you with the essential prerequisites before we start to work on Intelligent Content curation in ChatGPT:Access to the ChatGPT API.A Python environment is installed on your system.Required Python libraries: openai, requests.You must install the basic libraries and create an environment in order to get started. To access the ChatGPT API, you're going to have to use an ‘openai’ library. Install it using pip:pip install openai requestsThen, in the code examples, replace "YOUR_API_key" with your actual key to obtain a ChatGPT API key.Hands-on Examples1. Basic Content CurationExample 1: Curating News HeadlinesIn this first task, we're going to focus on content curation and sorting out news headlines that relate to a specific topic. We'll request a list of news stories based on this theme to be generated by ChatGPT.Input code:import openai api_key = "YOUR_API_KEY" # Function to curate news headlines def curate_news_headlines(topic):    openai.api_key = api_key    response = openai.Completion.create(        engine="davinci",        prompt=f"Curate news headlines about {topic}:\n- ",        max_tokens=100    )    return response.choices[0].text.strip() # Test news headline curation topic = "artificial intelligence" curated_headlines = curate_news_headlines(topic) print(curated_headlines) Output:Example 2: Curating Product DescriptionsLet's examine content curation when describing a product with this example. For an e-shop platform, you can use the ChatGPT tool to draw up attractive product descriptions.Input code:# Function to curate product descriptions def curate_product_descriptions(products):    openai.api_key = api_key    response = openai.Completion.create(        engine="davinci",        prompt=f"Create product descriptions for the following products:\n1. {products[0]}\n2. {products[1]}\n3. {products[2]}\n\n",        max_tokens=200    )    return response.choices[0].text.strip() # Test product description curation products = ["Smartphone", "Laptop", "Smartwatch"] product_descriptions = curate_product_descriptions(products) print(product_descriptions)Output:2. Enhancing Content Curation with ChatGPTExample 1: Generating Blog Post IdeasThe curation process does not exclusively focus on the selection of current content; it can also include developing ideas to produce new ones. We're going to ask ChatGPT to come up with blog posts for a specific niche in this example.Input code:# Function to generate blog post ideas def generate_blog_post_ideas(niche):    openai.api_key = api_key    response = openai.Completion.create(        engine="davinci",        prompt=f"Generate blog post ideas for the {niche} niche:\n- ",        max_tokens=150    )    return response.choices[0].text.strip() # Test blog post idea generation niche = "digital marketing" blog_post_ideas = generate_blog_post_ideas(niche) print(blog_post_ideas)Output:Example 2: Automated Content SummarizationA summary of lengthy articles or reports is often part of the advanced content curation process. In order to speed up content summarization, ChatGPT can be used.Input code:# Function for automated content summarization def automate_content_summarization(article):    openai.api_key = api_key    response = openai.Completion.create(        engine="davinci",        prompt=f"Summarize the following article:\n{article}\n\nSummary:",        max_tokens=150    )    return response.choices[0].text.strip() # Test automated content summarization article = "In a recent study, researchers have made significant progress in understanding the effects of climate change on polar bear populations. The study, conducted over five years, involved tracking bear movements and monitoring ice floe patterns." summary = automate_content_summarization(article) print(summary)Output:"In a recent study, researchers have made significant progress in understanding the effects of climate change on polar bear populations. The study, conducted over five years, involved tracking bear movements and monitoring ice floe patterns. Summary: The study's findings shed light on the impact of climate change on polar bear populations and their habitats, providing valuable insights into their conservation."Example 3: Customized Content GenerationCustomized content generation, such as the creation of personalized newsletters, may be required for advanced content curation. The ChatGPT service can assist with the creation of custom content.Input Code:# Function for generating a customized newsletter def generate_customized_newsletter(user_interests):    openai.api_key = api_key    response = openai.Completion.create(        engine="davinci",        prompt=f"Create a customized newsletter for a user interested in {', '.join(user_interests)}:\n\n",        max_tokens=500    )    return response.choices[0].text.strip() # Test customized newsletter generation user_interests = ["technology", "AI", "blockchain"] customized_newsletter = generate_customized_newsletter(user_interests) print(customized_newsletter)Output:ConclusionIn conclusion, ChatGPT can be a useful tool for intelligent curation of content. It showed how the ChatGPT could help with tasks such as the generation of query queries, identification of pertinent content, selection of best content, and collection of collected information through examples and insight. If you have the right setup and prompt, it can be a powerful resource for content creators and marketing agents who want to provide audiences with more relevant and enjoyable content, making ChatGPT an efficient way of streamlining their content curation process. Make sure you adapt the code and examples for your content curation needs, and continue experimenting to further improve your Content Curation Strategy.Author BioSangita Mahala is a passionate IT professional with an outstanding track record, having an impressive array of certifications, including 12x Microsoft, 11x GCP, 2x Oracle, and LinkedIn Marketing Insider Certified. She is a Google Crowdsource Influencer and IBM champion learner gold. She also possesses extensive experience as a technical content writer and accomplished book blogger. She is always Committed to staying with emerging trends and technologies in the IT sector.

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!IntroductionGenerative AI is a subset of artificial intelligence that trains models to generate new data similar to some existing data. Examples - are image generation - creating realistic images that do not exist, Text generation - generating human-like text based on a given prompt, and music composition- creating new music compositions based on existing styles and genres.LLM - Large Language models - are a type of AI model specialized in processing and generating human language - They are trained on vast amounts of text data, which makes them capable of understanding context, semantics, and language nuances. Example- GPT3 from OPENAI.LLMs automate routine language processing tasks - freeing up human resources for more strategic work.Black Box DilemmaComplex ML models, like deep neural networks, are often termed as "black boxes" due to their opaque nature. While they can process vast amounts of data and provide accurate predictions, understanding how they arrived at a particular decision is challenging.Transparency in ML models is crucial for building trust, verifying results, and ensuring that the model is working as intended. It's also necessary for debugging and improving models.Model Explainability LandscapeModel explainability refers to the degree to which a human can understand the decisions made by a machine learning model. It's about making the model’s decisions interpretable to humans, which is crucial for trust and actionable insights. There are two types of explainability models -Intrinsic explainability refers to models that are naturally interpretable due to their simplicity and transparency. They provide insight into their decision-making process as part of their inherent design.Examples: Decision Trees, Linear Regression, Logistic Regression.Pros and Cons: Highlight that while they are easy to understand, they may lack the predictive power of more complex models.Post-hoc explainability methods are applied after a model has been trained. They aim to explain the decisions of complex, black-box models by approximating their behavior or inspecting their structure.Examples: LIME (Local Interpretable Model-agnostic Explanations), SHAP (SHapley Additive exPlanations), and Integrated Gradients.Pros and Cons: Post-hoc methods allow for the interpretation of complex models but the explanations provided may not always be perfect or may require additional computational resources.SHAP (SHapley Additive exPlanations)Concept:Main Idea: SHAP values provide a measure of the impact of each feature on the prediction for a particular instance.Foundation: Based on Shapley values from cooperative game theory.Working Mechanism:Shapley Value Calculation:For a given instance, consider all possible subsets of features.For each subset, compare the model's prediction with and without a particular feature.Average these differences across all subsets to compute the Shapley value for that feature.SHAP Value Interpretation:Positive SHAP values indicate a feature pushing the prediction higher, while negative values indicate the opposite.The magnitude of the SHAP value indicates the strength of the effect.LIME (Local Interpretable Model-agnostic Explanations)Concept:Main Idea: LIME aims to explain the predictions of machine learning models by approximating the model locally around the prediction point.Model-Agnostic: It can be used with any machine learning model.Working Mechanism:Selection of Data Point: Select a data point that you want to explain.Perturbation: Create a dataset of perturbed instances by randomly changing the values of features of the original data point.Model Prediction: Obtain predictions for these perturbed instances using the original model.Weight Assignment: Assign weights to the perturbed instances based on their proximity to the original data point.Local Model Training: Train a simpler, interpretable model (like linear regression or decision tree) on the perturbed dataset, using the weights from step 4.Explanation Extraction: Extract explanations from the simpler model, which now serves as a local surrogate of the original complex model.Hands-on exampleIn the below code snippet, we are using a popular churn prediction dataset to create a Random Forest Model.# Part 1 - Data Preprocessing # Importing the libraries import numpy as np import matplotlib.pyplot as plt import pandas as pd # Importing the dataset dataset = pd.read_csv('Churn_Modelling.csv') X = dataset.iloc[:, 3:13] y = dataset.iloc[:, 13] dataset.head()#Create dummy variables geography=pd.get_dummies(X["Geography"],drop_first=True) gender=pd.get_dummies(X['Gender'],drop_first=True)## Concatenate the Data Frames X=pd.concat([X,geography,gender],axis=1) ## Drop Unnecessary columns X=X.drop(['Geography','Gender'],axis=1)Now, we save the model pickle file and use the lime and shap libraries for explainability.import pickle pickle.dump(classifier, open("classifier.pkl", 'wb')) pip install lime pip install shap import lime from lime import lime_tabular interpretor = lime_tabular.LimeTabularExplainer( training_data=np.array(X_train), feature_names=X_train.columns, mode='classification')Lime has a Lime Tabular module to set the explainability module for tabular data. We pass in the training dataset, and the mode of the model as classification here.exp = interpretor.explain_instance( data_row=X_test.iloc[5], ##new data predict_fn=classifier.predict_proba) exp.show_in_notebook(show_table=True) We can see from the above chart, that Lime is able to explain one particular prediction from X_test in detail. The prediction here is 1 - (Churn is True), and the features that are contributing positively are represented in orange, and negatively are shown in blue.import shap shap.initjs() explainer = shap.Explainer(classifier) shap_values = explainer.shap_values(X_test) shap.summary_plot(shap_values, X_test)In the above code snippet, we have created a plot for explainability using the shap library. The Shap library here gives a global explanation for the entire test dataset, compared to LIME which focuses on local interpretation.From the below graph, we can see which features contribute to how much for each of the churn classes.ConclusionExplainability in AI enables trust in AI systems, and enables us to dive deeper in understanding the reasoning behind the models, and make appropriate updates to models in case there are any biases. In this article, we used libraries such as SHAP and LIME that make explainability easier to design and implement.Author BioSwagata Ashwani serves as a Principal Data Scientist at Boomi, where she leads the charge in deploying cutting-edge AI solutions, with a particular emphasis on Natural Language Processing (NLP). With a stellar track record in AI research, she is always on the lookout for the next state-of-the-art tool or technique to revolutionize the industry. Beyond her technical expertise, Swagata is a fervent advocate for women in tech. She believes in giving back to the community, regularly contributing to open-source initiatives that drive the democratization of technology.Swagata's passion isn't limited to the world of AI; she is a nature enthusiast, often wandering beaches and indulging in the serenity they offer. With a cup of coffee in hand, she finds joy in the rhythm of dance and the tranquility of the great outdoors.

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!IntroductionAs data volumes grow exponentially, converting vast amounts of data into digestible, human-friendly "information assets" has become a blend of art and science. This process requires understanding the data's context, the intended objective, the target audience. The final step of curating and converting information assets into a cohesive narrative that results in human understanding and persuasion is known as “Data Storytelling.”The Essence of Data StorytellingCrafting a compelling data story means setting a clear objective, curating information, and narrating relevant information assets. Good stories communicate effectively, leaving a lasting impression, and in some cases, sway opinions or reinforce previously accepted knowledge. A common tactic to tell an influential data story typically contains quantitative facts and statistics.Modern enterprises utilize a myriad of techniques to convert accumulated data into valuable information assets.  The vast amounts of analytical and qualitative information assets get distributed in the form of reports and dashboards. These assets are informational to support process automation, decision support, and facilitate future predictions.  Many curated stories manifest as summarized text and charts, often taking the form of a document, spreadsheet, or presentation.The Potential of Generative AIWhile analytics and machine learning are adept at tackling large-scale computational challenges, the growing demand for curated explanations has elevated data storytelling to an important discipline. However, the number of skilled data storytellers remains limited due to the unique blend of technical and soft skills required. This has led to a high demand for quality data stories and a scarcity of adept professionals. Generative AI provides a promising solution by automating many of the data storytelling steps and simulating human interpretation and comprehension.Crafting Dynamic Narratives with AIConstructing a coherent narrative from data is intricate. However, the emergence of powerful generative AI models, such as BARD and ChatGPT, offers hope. These platforms, initially popularized by potent large language models, have recently incorporated computer vision to interpret and generate visual data. Given the right context and curated data, these AI models seem ready to address several challenges of data storytelling.Presently, publicly available generative AI models can produce code-based data analysis, interpret and present findings, experiment with story structures, extract insights, and customize messages based on user personas. Generative AI isn’t adapted to prepare complete visual stories with quantitative data. Merging and chaining multiple AI agents will open the door to translating and delivering visual stories.The essence of data storytelling lies in narration, and occasionally, in persuasion or creative representation of information. Unmonitored nuances in AI-generated data narratives can be misleading or flat-out incorrect. There is no scenario where data storytelling should go un-aided by a human reviewer.How Generative AI Can Aid Traditional Descriptive Analytics StoriesVarious data analysis techniques, including scatter plots, correlation mattresses, and histograms, help unveil the relationships and patterns within the data. Identifying trends and visualizing the data distribution process, the exploratory data analysis technique guides in selecting the appropriate analysis method. Large language models are not designed to perform computational and quantitative analysis. However, they are quite proficient at generating coding capable of and translating questions to code into quantitative analysis only when context and data are well defined.Understanding Data ContextBefore choosing any technique to generate narratives, it's essential to understand the context of a dataset. Delving deep into domain-specific nuances ensures narratives that are both focused and relevant.● Preprocessing and Data CleaningClean and well-processed data form the bedrock of impactful analyses. Both human-built data stories and AI-augmented narratives falter with poor data. Advancements in data management, governance, and descriptive analytics are pivotal in paving the way for superior AI-enhanced stories. Proper preprocessing and quality data are prerequisites for crafting reliable narratives.● Exploratory Data Analysis TechniquesData exploration without context and understanding of the data is challenging. However, with a very good dictionary, business glossary, and good data large language models can easily write code capable of streamlining basic data exploration with a human in the loop steering the process.● Machine Learning with Advanced AnalyticsTo create dynamic and impactful narratives, it's vital to employ a range of advanced analytical methods, such as regression analysis and clustering. These techniques help unearth detailed insights. Similarly, Generative AI can efficiently conduct basic statistical analyses. It can also deploy machine learning algorithms like decision trees and random forests to discern intricate patterns. Weaving these insights into narrative techniques imparts a layer of sophistication and depth to the storytelling.● Interactive Data VisualizationInteractive visualization tools enable users to dynamically explore data. Traditionally, this area has been predominantly the domain of human analysis. While machines can interpret data patterns without visual aids, rapid advancements in computer vision and generative AI are bridging the gap. Today's AI can interpret visual representations of data, deriving meaning from visualizations. It's becoming increasingly common for dashboards to be reviewed and summarized by AI, a trend likely to continue.ExampleHere is a simple example in action where we have a data insight that we want to translate to a simple infographic:Data Insight: Our customer demographics include 45% millennials, 30% Gen X, and 25% baby boomers.Prompt: Create a visual representation to tell the story via wolframResult: The result with Wolram enabled is a conversion of my prompt and data insights into code that can be translated to a visual infographic REQUEST TO WOLFRAM{ "input": "BarChart[{45, 30, 25}, ChartLabels -> {\"Millennials\", \"Gen X\", \"Baby Boomers\"}, ChartStyle -> {RGBColor[0, 0.5, 1], RGBColor[0, 0.8, 0], RGBColor[1, 0.5, 0]}, PlotLabel -> \"Customer Demographics\", AxesLabel -> {\"Demographic\", \"Percentage\"}]" }RESPONSE FROM WOLFRAM{ "output": "https://www.wolframcloud.com/obj/86f3ba22-6a46-412d-88e1-b90064001407" }How Generative AI Can Aid Narrative CreationNatural language processing One of the techniques to generate dynamic narratives is the implementation of natural language processing. The technique helps in extracting insights from contextual data. Sometimes, sentiment analysis and topic modeling help in processing textual information while changing the outlook of unstructured data into valuable narratives. At the same time, integrating the same analysis can increase understanding of the narrative.Data storytellingAs a user, if you want to draft a compelling data story, you need to structure the narrative coherently. Starting with a captivating introduction accompanied by data-driven insights and their implications enhances the method. Such compelling storytelling ensures that the focal message is conveyed with clarity.Data Insight: Our website traffic increased by 25% after the recent marketing campaign. Sales of Product A saw a significant boost, contributing to 40% of total revenue.Prompt: Create a visual representation to tell the story via wolfram and a data-driven story based on the following insightsResult:REQUEST TO WOLFRAM:{ "input": "GraphicsRow[{BarChart[{100, 125}, ChartLabels -> {\"Before Campaign\", \"After Campaign\"}, ChartStyle -> {Blue, Green}, AxesLabel -> {\"\", \"Traffic\"}, PlotLabel -> \"Website Traffic\"], PieChart[{40, 60}, ChartLabels -> {\"Product A\", \"Other Products\"}, ChartStyle -> {Red, Yellow}, PlotLabel -> \"Revenue Contribution\"]}]" }RESPONSE FROM WOLFRAM:{ "output": "https://www.wolframcloud.com/obj/7221f424-771c-4a02-997b-f9bf8fade843" }Narrative: "After launching our recent marketing campaign, we witnessed a remarkable surge in our website traffic. The numbers speak for themselves: a significant 25% increase! This not only brought more visitors to our platform but also translated into tangible business outcomes. A standout performer was Product A, which became a major revenue driver. It alone accounted for a whopping 40% of our total revenue, highlighting its immense popularity among our customers. The campaign not only amplified our reach but also reinforced the market's love for Product A. It's evident that our marketing efforts and Product A together created a winning combination for our business."How can AI-aided storytelling drive operations efficiently?AI-aided storytelling methods don’t always require creating information assets from raw data. Generative AI can assist in translating previously created information assets or summaries into the new explorative analyses.Data Insight: In the last quarter, our sales increased by 20%. Customer engagement on social media platforms grew by 35%.Prompt: How can we summarize the information to tell a good data story? What are some additional insights we should investigate?Result from Large Language Model:Additional Insights to Investigate: 1. Source of Traffic and Sales: Which social media platforms contributed the most to the increased engagement? Are there specific platforms that are driving more sales compared to others? 2. Content Analysis: What type of content (videos, articles, images, etc.) received the most engagement? Were there specific campaigns or promotions that were particularly successful? 3. Customer Demographics: Has there been a change in the demographics of the customers who are purchasing? Are younger or older age groups more engaged on social media?From the above prompt and resulting information it is clear how generative AI helps in crafting the story and further exploring complex information relationships in ways that previously required expertise. As a result, large lanague models in the exploration phase can help:Improve the exploration ideas and hypothesis.Creating quck infographicsAutomate the manual process of narrative creation and summarization.Insights of leveraging AI for more effective data communication There can be various ways of leveraging artificial intelligence to incorporate data communication effectively. These are the top areas where generative AI can help boost the operation.Streamlined Unstructured Data AnalysisLarge language models are very good at ingesting and interpreting bodies of text and interpreting and extracting summarized facts. The latest generative AI tools, including OpenAI, now support interpreting large documents including PDFs and spreadsheets as large as 100MB. For example, you can potentially import and interpret an annual statement and summarize it. For large data sets representing many documents or gigabytes of data, there are a number of ingest, indexing, and large language model integration techniques to effectively expand the body of knowledge and context your model has to formulate data stories.Advancing analyticsDemocratization of data science is further expanded thanks to large language models’ ability to interpret and write code, notably Python. Using code interpreter, we can easily feed AI tens or hundreds of thousands of data points and predict the most predictive variables that determine a target variable. For example, after importing 50,000 records and feeding the following prompt a modern AI model can automatically perform steps traditionally carried out by a data analyst or data scientist. To successfully use this method, you still need to understand and interpret results and direct any miscalculations. For example, the AI model will not help you understand variables that overfit.The steps we'll follow are:1. Data Preprocessing:Handle missing values.Encode categorical variables.Split the dataset into training and testing sets.2. Train a model (e.g., random forest) using the data.3. Extract feature importance to determine the most predictive variables.Resulting code example: # Redefining the RandomForestClassifier rf = RandomForestClassifier(n_estimators=100, random_state=42) # Training a RandomForest model rf.fit(X_train, y_train) # Extracting feature importances feature_importances = rf.feature_importances_ sorted_idx = feature_importances.argsort()[-10:][::-1]  # We'll display the top 10 features top_features = X.columns[sorted_idx] top_importances = feature_importances[sorted_idx] top_features, top_importancesResulting code example:Based on the Random Forest model, the top 10 most predictive variables for "Bad" are…ConclusionThere are numerous opportunities to utilize generative AI to aid data storytelling. While the techniques covered in this article help streamline the process, there is no magic easy button to automate data storytelling end to end. In the world of data storytelling, AI is a perfect augmentation tool to craft a narrative, perform analysis, and inspire new ideas. However, the competitive and disruptive world of AI is already merging computer vision, code interpretation, and generative AI further opening the door for new techniques. As images and text converge with code interpretation, we should see new distributive storytelling tools emerge. The most important ingredient is always the data itself. I am excited to continue to explore and experiment with these advancements to streamline my data storytelling abilities.Author BioRyan Goodman has dedicated 20 years to the business of data and analytics, working as a practitioner, executive, and entrepreneur. He recently founded DataTools Pro after 4 years at Reliant Funding, where he served as the VP of Analytics and BI. There, he implemented a modern data stack, utilized data sciences, integrated cloud analytics, and established a governance structure. Drawing from his experiences as a customer, Ryan is now collaborating with his team to develop rapid deployment industry solutions. These solutions utilize machine learning, LLMs, and modern data platforms to significantly reduce the time to value for data and analytics teams.

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!IntroductionLarge Language Models, or LLMs for short, are becoming a big deal in the world of technology. They're powerful and can do a lot, but they're not always easy to handle. Just like when building a big tower, you want to make sure everything goes right from the start to the finish. That's where Weights & Biases, often called W&B, comes in. It's a tool that helps people keep an eye on how their models are doing. In this article, we'll talk about why it's so important to watch over LLMs, how W&B helps with that, and how to use it. Let's dive in!Large Language Models (LLMs)Large Language Models (LLMs) are machine learning models trained on vast amounts of text data to understand and generate human-like text. They excel in processing and producing language, enabling various applications like translation, summarization, and conversation.LLMs, such as GPT-3 by OpenAI, utilize deep learning architectures to learn patterns and relationships in the data, making them capable of sophisticated language tasks. Through training on diverse datasets, they aim to comprehend context, semantics, and nuances akin to human communication.When discussing the forefront of natural language processing, several Large Language Models (LLMs) consistently emerge: The Need for Debugging & Monitoring LLMsUnderstanding and overseeing Large Language Models (LLMs) is much like supervising an intricate machine: they're powerful, and versatile, but require keen oversight.Firstly, think about the intricacy of LLMs. They far surpass the complexity of your typical day-to-day machine learning models. While they hold immense potential to revolutionize tasks involving language - think customer support, content creation, and translations - their intricate designs can sometimes misfire. If we're not careful, instead of a smooth conversation with a chatbot, users might encounter bewildering responses, leading to user frustration and diminished trust.Then there's the matter of resources. Training LLMs isn't just about the time; it's also financially demanding. Each hiccup, if not caught early, can translate to unnecessary expenditures. It's much like constructing a skyscraper; mid-way errors are costlier to rectify than those identified in the blueprint phase.Introduction to Weights & BiasesSourceWeights & Biases (W&B) is a cutting-edge platform tailored for machine learning practitioners. It offers a suite of tools designed to help streamline the model development process, from tracking experiments to visualizing results.With W&B, researchers and developers can efficiently monitor their LLM training progress, compare different model versions, and collaborate with team members. It's an invaluable asset for anyone looking to optimize and scale their machine-learning workflows.How to Use W&B for Debugging & Monitoring LLMsIn the hands-on section of this article, we will adhere to the following structured approach, illustrated in the diagram below. We will fine-tune our model and leverage Weights and biases to save critical metrics, tables, and visualizations. This will empower us with deeper insights, enabling efficient debugging and monitoring of our Large Language Models. 1. Setting up Weights and Biasesa. Importing Necessary Librariesimport torch import wandb from transformers import BertTokenizer, BertForSequenceClassification from torch.utils.data import DataLoader, random_split from datasets import load_datasetIntizailaizing W&B # Initialize W&B wandb.init(project='llm_monitoring', name='bert_example')b. Loading the BERT Model# Load tokenizer and model tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') model = BertForSequenceClassification.from_pretrained('bert-base-uncased')2. Fine-tuning your Modela. Loading your datasetdataset = load_dataset('Load your dataset')b. Fine-tuning the modelfor epoch in range(config.epochs):    model.train()    for batch in train_dataloader:       # ……….       # Continue training process here       # ………..3. Tracking Metrics# Log the validation metrics to W&B    wandb.log({        "Epoch": epoch,        "Validation Loss": avg_val_loss,        "Validation Accuracy": val_accuracy    })4. Graph Visualizationsa. Plotting and logging Training Loss Graphfig, ax = plt.subplots(figsize=(10,5)) ax.plot(train_losses, label="Training Loss", color='blue') ax.set(title="Training Losses", xlabel="Epoch", ylabel="Loss") wandb.log({"Training Loss Curve": wandb.Image(fig)})b. Plotting and logging Validation Loss Graphfig, ax = plt.subplots(figsize=(10,5)) ax.plot(val_losses, label="Validation Loss", color='orange') ax.set(title="Validation Losses", xlabel="Epoch", ylabel="Loss") wandb.log({"Validation Loss Curve": wandb.Image(fig)})c. Plotting and Log Validation Accuracy Graphfig, ax = plt.subplots(figsize=(10,5)) ax.plot(val_accuracies, label="Validation Accuracy", color='green') ax.set(title="Validation Accuracies", xlabel="Epoch", ylabel="Accuracy") wandb.log({"Validation Accuracy Curve": wandb.Image(fig)})d. Plotting and Log Training Accuracy Graphfig, ax = plt.subplots(figsize=(10,5)) ax.plot(train_accuracies, label="Training Accuracy", color='blue') ax.set(title="Training Accuracies", xlabel="Epoch", ylabel="Accuracy") wandb.log({"Training Accuracy Curve": wandb.Image(fig)})5. Manual Checkupsquestions = ["What's the weather like?", "Who won the world cup?", "How do you make an omelette?", "Why is the sky blue?", "When is the next holiday?"] old_model_responses = ["It's sunny.", "France won the last one.", "Mix eggs and fry them.", "Because of the atmosphere.", "It's on December 25th."] new_model_responses = ["The weather is clear and sunny.", "Brazil was the champion in the previous world cup.", "Whisk the eggs, add fillings, and cook in a pan.", "Due to Rayleigh scattering.", "The upcoming holiday is on New Year's Eve."] # Create a W&B Table table = wandb.Table(columns=["question", "old_model_response", "new_model_response"]) for q, old, new in zip(questions, old_model_responses, new_model_responses):    table.add_data(q, old, new) # Log the table to W&B wandb.log({"NLP Responses Comparison": table}) 6. Closing the W&B run after all logs are uploadedwandb.finish()ConclusionLarge Language Models have truly transformed the landscape of technology. Their vast capabilities are nothing short of amazing, but like all powerful tools, they require understanding and attention. Fortunately, with platforms like Weights & Biases, we have a handy toolkit to guide us. It reminds us that while LLMs are game-changers, they still need a bit of oversight.Author BioMostafa Ibrahim is a dedicated software engineer based in London, where he works in the dynamic field of Fintech. His professional journey is driven by a passion for cutting-edge technologies, particularly in the realms of machine learning and bioinformatics. When he's not immersed in coding or data analysis, Mostafa loves to travel.Medium

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!Understanding customer needs is critical for business success. Segmenting customers into groups with common traits allows for targeting products, marketing, and services. This guide will walk through customer segmentation using PandasAI, a Python library that makes the process easy and accessible.OverviewWe'll work through segmenting sample customer data step-by-step using PandasAI's conversational interface. Specifically, we'll cover:Loading and exploring customer dataSelecting segmentation featuresDetermining the optimal number of clustersPerforming clustering with PandasAIAnalyzing and describing the segmentsFollow along with the explanations and code examples below to gain hands-on experience with customer segmentation in Python.IntroductionCustomer segmentation provides immense value by enabling tailored messaging and product offerings. But for many, the technical complexity makes segmentation impractical. PandasAI removes this barrier by automating the process via simple conversational queries.In this guide, we'll explore customer segmentation hands-on by working through examples using PandasAI. You'll learn how to load data, determine clusters, perform segmentation, and analyze results. The steps are accessible even without extensive data science expertise. By the end, you'll be equipped to leverage PandasAI's capabilities to unlock deeper customer insights. Let's get started!Step 1 - Load Customer DataWe'll use a fictional customer dataset customers.csv containing 5000 rows with attributes like demographics, location, transactions, etc. Let's load it with Pandas:import pandas as pd customers = pd.read_csv("customers.csv")Preview the data:customers.head()This gives us a sense of available attributes for segmentation.Step 2 - Select Segmentation FeaturesNow we need to decide which features to use for creating customer groups. For this example, let's select:AgeGenderCityNumber of TransactionsExtract these into a new DataFrame:segmentation_features = ['age', 'gender', 'city', 'transactions'] customer_data = customers[segmentation_features]Step 3 - Determine Optimal Number of ClustersA key step is choosing the appropriate number of segments k. Too few reduces distinction, too many makes them less useful.Traditionally, without using PandasAI, we should apply the elbow method to identify the optimal k value for the data. Something like this:from sklearn.cluster import KMeans from sklearn.preprocessing import OneHotEncoder from sklearn.impute import SimpleImputer import pandas as pd import matplotlib.pyplot as plt # Handle missing values by imputing with the most frequent value in each column imputer = SimpleImputer(strategy='most_frequent') df_imputed = pd.DataFrame(imputer.fit_transform(customers), columns=customers.columns) # Perform one-hot encoding for the 'gender' and 'city' columns encoder = OneHotEncoder(sparse=False) gender_city_encoded = encoder.fit_transform(df_imputed[['gender', 'city']]) # Concatenate the encoded columns with the original DataFrame df_encoded = pd.concat([df_imputed, pd.DataFrame(gender_city_encoded, columns=encoder.get_feature_names_out(['gender', 'city']))], axis=1) # Drop the original 'gender' and 'city' columns as they're no longer needed after encoding df_encoded.drop(columns=['gender', 'city'], inplace=True) # Calculate SSE for k = 1 to 9 sse = {} for k in range(1, 9): km = KMeans(n_clusters=k) km.fit(df_encoded) sse[k] = km.inertia_ # Plot elbow curve plt.plot(list(sse.keys()), list(sse.values())) plt.xlabel("Number of Clusters") plt.ylabel("SSE") plt.show()Examining the elbow point, 4 seems a good number of clusters for this data, so we’ll create 4 clusters.Too complicated? You can easily let PandasAI do it for you.customers.chat("What is the ideal amount of clusters for the given dataset?") # 4 PandasAI will use a silhouette score under the hood to calculate the optimal amount of clusters based on your data.Silhouette score is a metric used to evaluate the goodness of a clustering model. It measures how well each data point fits within its assigned cluster versus how well it would fit within other clusters.PandasAI leverages silhouette analysis to pick the optimal number of clusters for k-means segmentation based on which configuration offers the best coherence within clusters and distinction between clusters for the given data.Step 4 - Perform Clustering with PandasAINow we'll use PandasAI to handle clustering based on the elbow method insights.First import and initialize a SmartDataFrame:from pandasai import SmartDataframe sdf = SmartDataframe(customers)Then simply ask PandasAI to cluster:segments = sdf.chat(""" Segment customers into 4 clusters based on their age, gender, city and number of transactions. """)This performs k-means clustering and adds the segment labels to the original data. Let's inspect the results:print(segments)Each customer now has a cluster segment assigned.Step 5 - Analyze and Describe ClustersWith the clusters created, we can derive insights by analyzing them:centers = segments.chat("Show cluster centers") print(centers)Step 6 - Enrich Analysis with Additional DataOur original dataset contained only a few features. To enhance the analysis, we can join the clustered data with additional customer info like:Purchase historyCustomer lifetime valueEngagement metricsProduct usageRatings/reviewsBringing in other datasets allows drilling down into each segment with a deeper perspective.For example, we could join the review history and analyze customer satisfaction patterns within each cluster:# Join purchase data to segmented dataset enriched_data = pd.merge(segments, reviews, on='id') # Revenue for each cluster enriched_data.groupby('cluster').review_score.agg(['mean', 'median', 'count'])This provides a multidimensional view of our customers and segments, unlocking richer insights and enabling a more in-depth analysis for additional aggregate metrics for each cluster.ConclusionIn this guide, we worked through segmenting sample customer data step-by-step using PandasAI. The key aspects covered were:Loading customer data and selecting relevant featuresUsing the elbow method to determine the optimal number of clustersPerforming k-means clustering via simple PandasAI queriesAnalyzing and describing the created segmentsSegmentation provides immense value through tailored products and messaging. PandasAI makes the process accessible even without extensive data science expertise. By automating complex tasks through conversation, PandasAI allows you to gain actionable insights from your customer data.To build on this, additional data like customer lifetime value or engagement metrics could provide even deeper understanding of your customers. The key is asking the right questions – PandasAI handles the hard work to uncover meaningful answers from your data.Now you're equipped with hands-on experience leveraging PandasAI to simplify customer segmentation in Python.Author BioGabriele Venturi is a software engineer and entrepreneur who started coding at the young age of 12. Since then, he has launched several projects across gaming, travel, finance, and other spaces - contributing his technical skills to various startups across Europe over the past decade.Gabriele's true passion lies in leveraging AI advancements to simplify data analysis. This mission led him to create PandasAI, released open source in April 2023. PandasAI integrates large language models into the popular Python data analysis library Pandas. This enables an intuitive conversational interface for exploring data through natural language queries.By open-sourcing PandasAI, Gabriele aims to share the power of AI with the community and push boundaries in conversational data analytics. He actively contributes as an open-source developer dedicated to advancing what's possible with generative AI.

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!IntroductionIn the era of rapid technological advancements, speech recognition technology has emerged as a game-changer, revolutionizing how we interact with machines and devices.As we know, OpenAI has developed an exceptional Automatic Speech Recognition (ASR) system known as OpenAI Whisper.In this blog, we will deeply dive into Whisper, understanding its capabilities, applications, and how you can harness its power through the Whisper API.Understanding OpenAI WhisperWhat is OpenAI Whisper?Well, put-“You Speak…AI Writes” OpenAI Whisper is an advanced ASR system that converts spoken language into written text.Built on cutting-edge technology and trained on 680,000 hours of multilingual and multitask supervised data collected from the web, OpenAI Whisper excels in a wide range of speech recognition tasks, making it a valuable tool for developers and businesses.Why Does ASR (Automatic Speech Recognition) Matter?Automatic Speech Recognition (ASR) is not just a cutting-edge technology; it's a game-changer reshaping how we interact with our digital world. Imagine a world where your voice can unlock a wealth of possibilities. That's what ASR, with robust systems like Whisper leading the charge, has made possible.Let's dive deeper into the ASR universe.It's not just about making life more convenient; it's about leveling the playing field. ASR technology is like the magic wand that enhances accessibility for individuals with disabilities. It's the backbone of those voice assistants you chat with and the transcription services that make your voice immortal in text.But ASR doesn't stop there; it's a versatile tool taking over various industries. Picture this: ASR helps doctors transcribe patient records in healthcare with impeccable accuracy and speed. That means better care for you. And let's remember the trusty voice assistants like Siri and Google Assistant, always at your beck and call, answering questions and performing tasks, all thanks to ASR's natural language interaction wizardry. Setup and InstallationWhen embarking on your journey to harness the remarkable power of OpenAI Whisper for Automatic Speech Recognition (ASR), the first crucial step is to set up and install the necessary components.In this section, we will guide you through starting with OpenAI Whisper, ensuring you have everything in place to begin transcribing spoken words into text with astonishing accuracy.Prerequisites Before you dive into the installation process, it's essential to make sure you have the following prerequisites in order:OpenAI Account To access OpenAI Whisper, you must have an active OpenAI account. If you still need to sign up, visit the OpenAI website and create an account.API Key You will need an API key from OpenAI to make API requests. This key acts as your access token to use the Whisper ASR service. Ensure you have your API key ready; if you don't have one, you can obtain it from your OpenAI account.Development Environment It would help to have a functioning development environment for coding and running API requests. You can use your preferred programming language, Python, to interact with the Whisper API. Make sure you have the necessary libraries and tools installed.Installation Steps Now, let's walk through the steps to install and set up OpenAI Whisper for ASR:1. Install the OpenAI Python LibraryIf you haven't already, you must install the OpenAI Python library. This library simplifies the process of making API requests to OpenAI services, including Whisper. You can install it using pip, the Python package manager, by running the following command in your terminal: pip install openai2. Authenticate with Your API KeyYou must authenticate your requests with your API key to interact with the Whisper ASR service. You can do this by setting your API key as an environment variable in your development environment or by directly including it in your code. Here's how you can set the API key as an environment variable:import openai openai.api_key = "YOUR_API_KEY_HERE" Replace "YOUR_API_KEY_HERE" with your actual API key.3. Make API RequestsWith the OpenAI Python library installed and your API key adequately set, you can now start making API requests to Whisper. You can submit audio files or chunks of spoken content and receive transcriptions in response. import openai response = openai.Transcription.create( model="whisper", audio="YOUR_AUDIO_FILE_URL_OR_CONTENT", language="en-US"  # Adjust language code as needed ) print(response['text']) Replace "YOUR_AUDIO_FILE_URL_OR_CONTENT" with the audio source you want to transcribe.Testing Your SetupAfter following these installation steps, testing your setup with a small audio file or sample content is a good practice.This will help you verify that everything functions correctly and that you can effectively convert spoken words into text.Use Cases And ApplicationsTranscription ServicesWhisper excels in transcribing spoken words into text. This makes it a valuable tool for content creators, journalists, and researchers whose work demands them to convert audio recordings into written documents.Voice Assistants Whisper powers voice assistants and chatbots, enabling natural language understanding and interaction. This is instrumental in creating seamless user experiences in applications ranging from smartphones to smart home devices.AccessibilityWhisper enhances accessibility for individuals with hearing impairments by providing real-time captioning services during live events, presentations, and video conferences.Market ResearchASR technology can analyze customer call recordings, providing businesses with valuable insights and improving customer service.Multilingual SupportWhisper supports multiple languages, making it a valuable asset for global companies looking to reach diverse audiences.Making Your First API CallNow that you have your Whisper API key, it's time to make your first API call. Let's walk through a simple example of transcribing spoken language into text using Python.pythonCopy code:import openai # Replace 'your_api_key' with your actual Whisper API key openai.api_key = 'your_api_key' response = openai.Transcription.create( audio="<https://your-audio-url.com/sample-audio.wav>", model="whisper", language="en-US" ) print(response['text'])In this example, we set up the API key, specify the audio source URL, select the Whisper model, and define the language. The response['text'] contains the transcribed text from the audio.Use CasesLanguage DetectionOne of the remarkable features of OpenAI Whisper is its ability to detect the language being spoken.This capability is invaluable for applications that require language-specific processing, such as language translation or sentiment analysis.Whisper's language detection feature simplifies identifying the language spoken in audio recordings, making it a powerful tool for multilingual applications.TranscriptionTranscription is one of the most common use cases for Whisper. Whether you need to transcribe interviews, podcasts, or customer service calls, Whisper's accuracy and speed make it an ideal choice.Developers can integrate Whisper to automate transcription, saving time and resources.Supported LanguagesOpenAI Whisper supports many languages, making it suitable for global applications. Some supported languages include English, Spanish, French, German, Chinese, and others.Open AI supports all these languages as of now-Afrikaans, Arabic, Armenian, Azerbaijani, Belarusian, Bosnian, Bulgarian, Catalan, Chinese, Croatian, Czech, Danish, Dutch, English, Estonian, Finnish, French, Galician, German, Greek, Hebrew, Hindi, Hungarian, Icelandic, Indonesian, Italian, Japanese, Kannada, Kazakh, Korean, Latvian, Lithuanian, Macedonian, Malay, Marathi, Maori, Nepali, Norwegian, Persian, Polish, Portuguese, Romanian, Russian, Serbian, Slovak, Slovenian, Spanish, Swahili, Swedish, Tagalog, Tamil, Thai, Turkish, Ukrainian, Urdu, Vietnamese, and Welsh.Best Practices While Using WhisperWhen working with Whisper for longer-format audio or complex tasks, following best practices is essential. For instance, you can break longer audio files into shorter segments for improved accuracy. Additionally, you can experiment with different settings and parameters to fine-tune the ASR system according to your specific requirements.Here's a simple example of how to break down more extended audio for transcription:pythonCopy codeimport openai # Replace 'your_api_key' with your actual Whisper API key openai.api_key = 'your_api_key' # Divide the longer audio into segments audio_segments = [    "<https://your-audio-url.com/segment1.wav>",    "<https://your-audio-url.com/segment2.wav>",    # Add more segments as needed ] # Transcribe each segment separately for segment in audio_segments:    response = openai.Transcription.create(        audio=segment,        model="whisper",        language="en-US"    )    print(response['text'])These best practices and tips ensure you get the most accurate results when using OpenAI Whisper.ConclusionIn this blog, we've explored the incredible potential of OpenAI Whisper, an advanced ASR system that can transform how you interact with audio data. We've covered its use cases, how to access the Whisper API, make your first API call, and implement language detection and transcription. With its support for multiple languages and best practices for optimizing performance, Whisper is a valuable tool for developers and businesses looking to harness the power of automatic speech recognition.In our next blog post, we will delve even deeper into OpenAI Whisper, exploring its advanced features and the latest developments in ASR technology. Stay tuned for "Advances in OpenAI Whisper: Unlocking the Future of Speech Recognition."For now, start your journey with OpenAI Whisper by requesting access to the API and experimenting with its capabilities. The possibilities are endless, and the power of spoken language recognition is at your fingertips.Author BioVivekanandan, a seasoned Data Specialist with over a decade of expertise in Data Science and Big Data, excels in intricate projects spanning diverse domains. Proficient in cloud analytics and data warehouses, he holds degrees in Industrial Engineering, Big Data Analytics from IIM Bangalore, and Data Science from Eastern University.As a Certified SAFe Product Manager and Practitioner, Vivekanandan ranks in the top 1 percentile on Kaggle globally. Beyond corporate excellence, he shares his knowledge as a Data Science guest faculty and advisor for educational institutes. 

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!IntroductionIn the modern digital age, artificial intelligence has changed how we handle complex tasks, including image analysis. Advanced models like ChatGPT have made this process more interactive and insightful. Instead of a basic understanding, users can now guide the system through prompts to get a detailed analysis of an image. This approach helps in revealing both broad themes and specific details. In this blog, we will look at how ChatGPT responds to a series of prompts, demonstrating the depth and versatility of AI-powered image analysis. Let’s startHere's a step-by-step guide to doing image analysis with ChatGPT:1. PreparationEnsure you have the image in an accessible format, preferably a common format such as JPEG, PNG, etc.Ensure the content of the image is suitable for analysis and doesn't breach any terms of service.2. Upload the ImageUse the platform's interface to upload the image to ChatGPT.3. Specify Your RequirementsClearly mention what you are expecting from the analysis. For instance:Identify objects in the image.Analyze the colors used.Describe the mood or theme.Any other specific analysis.4. Receive the AnalysisChatGPT will process the image and provide an analysis based on the information and patterns it recognizes. 5. Ask Follow-up QuestionsIf you have further questions about the analysis or if you require more details, feel free to ask.6. Iterative Analysis (if required)Based on the feedback and results, you might want to upload another image or ask for a different type of analysis on the same image. Follow steps 2-5 again for this.7. Utilize the AnalysisUse the given analysis for your intended purpose, whether it's for research, personal understanding, design feedback, etc.8. Review and FeedbackReflect on the accuracy and relevance of the provided analysis. Remember, while ChatGPT can provide insights based on patterns, it might not always capture the nuances or subjective interpretations of an image.Now to perform the image analysis we have deployed the Chain prompting technique. Here’s an example:Chain Prompting: A Brief OverviewChain prompting refers to the practice of building a sequence of interrelated prompts that progressively guide an artificial intelligence system to deliver desired responses. By initiating with a foundational prompt and then following it up with subsequent prompts that build upon the previous ones, users can engage in a deeper and more nuanced interaction with the system.The essence of chain prompting lies in its iterative nature. Instead of relying on a single, isolated question, users employ a series of interconnected prompts that allow for refining, expanding, or branching the AI's output. This approach can be particularly useful in situations where a broad topic needs to be explored in depth, or when the user is aiming to extract multifaceted insights.For instance, in the domain of image analysis, an initial prompt might request a general description of an image. Subsequent prompts can then delve deeper into specific aspects of the image, ask for comparisons, or even seek interpretations based on the initial description. Now Let’s dissect the nature of prompts given in the example below for analysis. These prompts are guiding the system through a process of image analysis. Starting from a general interpretation, they progressively request more specific and actionable insights based on the content of the image. The final prompt adds a layer of self-reflection, asking the system to assess the nature of the prompts themselves.Prompt 1: Hey ChatGPT ...Can you read the image?The below roadmap was taken from the infographics shared on LinkedIn by Mr Ravit Jain and can be found here.Analysis: This prompt is a general inquiry to see if the system can extract and interpret information from the provided image. The user is essentially asking if the system has the capability to understand and process visual data.Response: Prompt 2: Can you describe the data science landscape based on the above image?Analysis: This prompt requests a comprehensive description of the content within the image, focusing specifically on the "data science landscape." The user is looking for an interpretation of the image that summarizes its main points regarding data science.Response:Prompt 3: Based on the above description generated from the image list top skills a fresher should have to be successful in a data science career.Analysis: This prompt asks the system to provide actionable advice or recommendations. Using the previously described content of the image, the user wants to know which skills are most essential for someone new ("fresher") to the data science field.Response:Prompt 4: Map the skills listed in the image to different career in data scienceAnalysis: This prompt requests a more detailed breakdown or categorization of the image's content. The user is looking for a mapping of the various skills mentioned in the image to specific career paths within data science.Response:Prompt 5: Map the skills listed in the image to different career in data science...Analyse these prompts and tell what they do for image analysisAnalysis: This prompt seems to be a combination of Prompt 4 and a meta-analysis request. The first part reiterates the mapping request from Prompt 4. The second part asks the system to provide a reflective analysis of the prompts themselves in relation to image analysis (which is what we're doing right now).ConclusionIn conclusion, image analysis, when used with advanced models like ChatGPT, offers significant benefits. Our review of various prompts shows that users can obtain a wide range of insights from basic image descriptions to in-depth interpretations and career advice. The ability to direct the AI with specific questions and modify the analysis based on prior answers provides a customized experience. As technology progresses, the potential of AI-driven image analysis will likely grow. For those in professional, academic, or hobbyist roles, understanding how to effectively engage with these tools will become increasingly important in the digital world.Author BioDr. Anshul Saxena is an author, corporate consultant, inventor, and educator who assists clients in finding financial solutions using quantum computing and generative AI. He has filed over three Indian patents and has been granted an Australian Innovation Patent. Anshul is the author of two best-selling books in the realm of HR Analytics and Quantum Computing (Packt Publications). He has been instrumental in setting up new-age specializations like decision sciences and business analytics in multiple business schools across India. Currently, he is working as Assistant Professor and Coordinator – Center for Emerging Business Technologies at CHRIST (Deemed to be University), Pune Lavasa Campus. Dr. Anshul has also worked with reputed companies like IBM as a curriculum designer and trainer and has been instrumental in training 1000+ academicians and working professionals from universities and corporate houses like UPES, CRMIT, and NITTE Mangalore, Vishwakarma University, Pune & Kaziranga University, and KPMG, IBM, Altran, TCS, Metro CASH & Carry, HPCL & IOC. With a work experience of 5 years in the domain of financial risk analytics with TCS and Northern Trust, Dr. Anshul has guided master's students in creating projects on emerging business technologies, which have resulted in 8+ Scopus-indexed papers. Dr. Anshul holds a PhD in Applied AI (Management), an MBA in Finance, and a BSc in Chemistry. He possesses multiple certificates in the field of Generative AI and Quantum Computing from organizations like SAS, IBM, IISC, Harvard, and BIMTECH.Author of the book: Financial Modeling Using Quantum Computing

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!IntroductionIn today’s data-driven world, businesses constantly seek ways to extract more value from their data. One of the key strategies to accomplish this is Data Enrichment.Data Enrichment involves enhancing your existing datasets with additional information, which can lead to improved decision-making, customer engagement, and personalized experiences. In this blog, we’ll explore how to automate data enrichment using Snowflake, a powerful data warehousing platform, and Generative AI techniques.Understanding Data EnrichmentData Enrichment is simply the practice of enhancing your existing datasets with additional and relevant information. This supplementary data can include demographic data, geographic data, social media profiles, and much more. The primary goal is to improve the quality and depth of your data - making it more valuable for analytics, reporting, and decision-making.Why Automate Data Enrichment?Automating data enrichment not only saves time and resources but also improves data quality, supports real-time updates, and helps organizations stay competitive in an increasingly data-centric world. Whether in e-commerce, finance, healthcare, marketing, or any other industry, automation can be a strategic advantage that allows you to extract greater value from your data.EfficiencyManual data enrichment is time-consuming and resource-intensive. Automation allows you to process large volumes of data rapidly, reducing the time and effort required.ConsistencyHuman errors are common when manually enriching data. Automation ensures the process is consistent and accurate, reducing the risk of errors affecting decision-making.ScalabilityAs your organization grows and accumulates more data, automating data enrichment ensures you can handle larger datasets without a proportional increase in human resources.Enhanced Data QualityAutomated processes can validate and cleanse data, leading to higher data quality. High-quality data is essential for meaningful analytics and reporting.Competitive AdvantageIn a competitive business landscape, having access to enriched and up-to-date data can give you a significant advantage. It allows for more accurate market analysis, better customer understanding, and smarter decision-making.PersonalizationAutomated data enrichment can support personalized customer experiences, which are increasingly crucial for businesses. It allows you to tailor content, product recommendations, and marketing efforts to individual preferences and behaviors.Cost-EfficiencyWhile there are costs associated with setting up and maintaining automated data enrichment processes, these costs can be significantly lower in the long run compared to manual efforts, especially as the volume of data grows.Compliance and Data SecurityAutomated processes can be designed to adhere to data privacy regulations and security standards, reducing the risk of data breaches and compliance issues.ReproducibilityAutomated data enrichment processes can be documented, version-controlled, and easily reproduced, making it easier to audit and track changes over time.Data VarietyAs the sources and formats of data continue to expand, automation allows you to efficiently handle various data types, whether structured, semi-structured, or unstructured.Snowflake for Data EnrichmentSnowflake, a cloud-based data warehousing platform, provides powerful features for data manipulation and automation. Snowflake at the basic can be used to:Create tables for raw data and enrichment data.Load data into these tables using the COPY INTO command.Create views to combine raw and enrichment data based on common keys.Code Examples: Snowflake Data EnrichmentCreate TablesIn Snowflake, create tables for your raw data and enrichment data with: -  Create a table for raw data CREATE OR REPLACE TABLE raw_data (             Id INT,             name  STRING,             email  STRING ); -  Create a table for enrichment data CREATE OR REPLACE TABLE enrichment_data (         email  STRING,         location STRING,         age INT ); Load Data: Loading raw and enrichment data into their respective tables. -  Load raw data COPY INTO raw_data (id, name, email) FROM @<raw_data_stage>/raw_data.csv FILE_FORMAT = (TYPE = CSV); -  Load enrichment data COPY INTO enrichment_data (email, location, age) FROM @<enrichment_data_stage>/enrichment_data.csv FILE_FORMAT = (TYPE = CSV);Automate Data EnrichmentCreate a view that combines raw and enrichment data.-  Create a view that enriches the raw data CREATE OR REPLACE VIEW enriched_data AS SELECT    rd.id,    rd.name,    ed.location,    ed.age, -  Use generative AI to generate a description for the enriched date <Generative_AI_function> (ed.location, ed.age) AS description FROM       raw_data  rd JOIN      enrichment_data ed ON      rd.email = ed.email;Leveraging Snowflake for Data EnrichmentUsing Snowflake for data enrichment is a smart choice, especially if your organization relies on this cloud-based data warehousing platform. Snowflake provides a robust set of features for data manipulation and automation, making it an ideal environment to enhance the value of your data. Here are a few examples of how you can use Snowflake for data enrichment:Data Storage and ManagementSnowflake allows you to store and manage your data efficiently by separating storage and computing resources, which provides a scalable and cost-effective way to manage large data sets. You can store your raw and enriched data within Snowflake, making it readily accessible for enrichment processes.Data EnrichmentYou can perform data enrichment by combining data from your raw and enrichment tables. By using SQL JOIN operations to bring together related data based on common keys, such as email addresses.-        Create a view that enriches the raw data CREATE OR REPLACE VIEW enriched_data AS SELECT    rd.id,    rd.name,    ed.location,    ed.age, FROM       raw_data  rd JOIN      enrichment_data ed ON      rd.email = ed.email;Schedule UpdatesAutomating data enrichment by the creation of scheduled tasks within Snowflake. You can set up tasks to run at regular intervals, ensuring that your enriched data remains up to date.- Example: Creating a scheduled task to update enriched data CREATE OR REPLACE TASK update_enriched_data WAREHOUSE = <your_warehouse> SCHEDULE = ‘1 DAY’ AS INSERT INTO enriched_data (id, name, location, age) SELECT     rd.id,     rd.name,     ed.location,     ed.age FROM      raw_data rd JOIN     enrichment_data ed ON    rd.email = ed.email;Security and ComplianceSnowflake provides robust security features and complies with various data privacy regulations. Ensure that your data enrichment processes adhere to the necessary security and compliance standards to protect sensitive information.Monitoring and OptimizationRegularly monitoring the performance of your data enrichment processes. Snowflake offers tools for monitoring query execution so you can identify and address any performance bottlenecks. Optimization here is one of the crucial factors to ensure efficient data enrichment.Real-World ApplicationsData Enrichment is a powerful tool that stands for versatility in its real-world applications. Organizations across various sectors use it to improve their data quality, decision-making process, customer experiences, and overall operational efficiency. By augmenting their datasets with additional information, these organizations gain a competitive edge and drive innovation in their respective industries:E-commerce and RetailProduct Recommendations: E-commerce platforms use data enrichment to analyze customer browsing and purchase history. These enriched customer profiles help generate personalized product recommendations, increasing sales and customer satisfaction.Inventory Management: Retailers leverage enriched supplier data to optimize inventory management, ensuring they have the right products in stock at the right time.Marketing and AdvertisingCustomer Segmentation: Marketers use enriched customer data to create more refined customer segments. This enables them to tailor advertising campaigns and messaging for specific audiences, leading to higher engagement rates.Ad Targeting: Enriched demographic and behavioral data supports precise ad targeting. Advertisers can serve ads to audiences most likely to convert, reducing ad spend wastage.Financial ServicesCredit Scoring: Financial institutions augment customer data with credit scores, employment history, and other financial information to assess credit risk more accurately.Fraud Detection: Banks use data enrichment to detect suspicious activities by analyzing transaction data enriched with historical fraud patterns.HealthcarePatient Records: Healthcare providers enhance electronic health records (EHR) with patient demographics, medical histories, and test results. This results in comprehensive and up-to-date patient profiles, leading to better care decisions.Drug Discovery: Enriching molecular and clinical trial data accelerates drug discovery and research, potentially leading to breakthroughs in medical treatments.Social Media and Customer SupportSocial Media Insights: Social media platforms use data enrichment to provide businesses with detailed insights into their followers and engagement metrics, helping them refine their social media strategies.Customer Support: Enriched customer profiles enable support teams to offer more personalized assistance, increasing customer satisfaction and loyalty.ConclusionAutomating data enrichment with Snowflake and Generative AI is a powerful approach for businesses seeking to gain a competitive edge through data-driven insights. By combining a robust data warehousing platform with advanced AI techniques, you can efficiently and effectively enhance your datasets. Embrace automation, follow best practices, and unlock the full potential of your enriched data.Author BioShankar Narayanan (aka Shanky) has worked on numerous different cloud and emerging technologies like Azure, AWS, Google Cloud, IoT, Industry 4.0, and DevOps to name a few. He has led the architecture design and implementation for many Enterprise customers and helped enable them to break the barrier and take the first step towards a long and successful cloud journey. He was one of the early adopters of Microsoft Azure and Snowflake Data Cloud. Shanky likes to contribute back to the community. He contributes to open source is a frequently sought-after speaker and has delivered numerous talks on Microsoft Technologies and Snowflake. He is recognized as a Data Superhero by Snowflake and SAP Community Topic leader by SAP.

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!👋 Hello ,Welcome to another scintillating edition of AI_Distilled, featuring recent advancements in training and fine-tuning LLMs, GPT and AI models for enhanced business outcomes. Let’s get started with this week’s news and analysis with an industry expert’s opinion. “For me, the biggest opportunity we have is AI. Just like the cloud transformed every software category, we think AI is one such transformational shift. Whether it's in search or our Office software.” - Satya Nadella, CEO, Microsoft.  AI is indeed the biggest opportunity for mankind, a paradigm shift that can fundamentally redefine everything we know across industries. Recent reports suggest Apple’s deployment of cloud-based and on-device edge AI in iPhones and iPads in 2024. Qualcomm’s newly unveiled Snapdragon Elite X chips will find use in Microsoft Windows “AI PCs” for AI acceleration of tasks ranging from email summarization to image creation. It’s remarkable how AI has disrupted even PC environments for everyday users.  This week, we’ve brought you industry developments including DALL·E 3 unveiling for ChatGPT Plus and Enterprise users, Universal Music Group suing Anthropic over copyrighted lyrics distribution, OpenAI in talks for $86 billion valuation, surpassing leading tech firms, and Mojo SDK’s availability for Macs, unleashing AI power on Apple Silicon.  Look out for our curated collection of AI secret knowledge and tutorials on PyTorch Edge unveiling ExecuTorch for on-device inference, scaling reinforcement learning with cloud TPUs, building an IoT sensor network with AWS IoT Core and Amazon DocumentDB, and deploying embedding models with Hugging Face inference endpoints. 📥 Feedback on the Weekly EditionWhat do you think of this issue and our newsletter?Please consider taking the short survey below to share your thoughts and you will get a free PDF of the “The Applied Artificial Intelligence Workshop” eBook upon completion. Complete the Survey. Get a Packt eBook for Free!Writer’s Credit: Special shout-out to Vidhu Jain for their valuable contribution to this week’s newsletter content!  Cheers,  Merlyn Shelley  Editor-in-Chief, Packt     SignUp | Advertise | Archives⚡ TechWave: AI/GPT News & Analysis👉 Apple Aims to Introduce Generative AI to iPhone and iPad in Late 2024: Tech analyst Jeff Pu suggests that Apple is planning to integrate generative AI into its devices, beginning as early as late 2024. Apple is expected to deploy a combination of cloud-based and on-device edge AI. This move is aimed at letting users automate complex tasks and enhance Siri's capabilities, possibly starting with iOS 18. Apple remains cautious about privacy and responsible use of AI, acknowledging potential biases and hallucinations. 👉 DALL·E 3 Unveiled for ChatGPT Plus and Enterprise Users: OpenAI has introduced DALL·E 3 in ChatGPT, offering advanced image generation capabilities for Plus and Enterprise users. This feature allows users to describe their desired images, and DALL·E 3 creates a selection of visuals for them to refine and iterate upon within the chat. OpenAI has incorporated safety measures to prevent the generation of harmful content. Moreover, they are researching a provenance classifier to identify AI-generated images.  👉 Universal Music Group Sues AI Company Anthropic Over Copyrighted Lyrics Distribution: Universal Music Group and music publishers have filed a lawsuit against Anthropic for distributing copyrighted lyrics through its AI model Claude 2. The complaint alleges that Claude 2 can generate lyrics closely resembling copyrighted songs without proper licensing, even when not explicitly prompted to do so. The music publishers claim that while other lyric distribution platforms pay to license lyrics, Anthropic omits essential copyright management information.  👉 Nvidia's Eureka AI Agent, Powered by GPT-4, Teaches Robots Complex Skills: Nvidia Research has introduced Eureka, an AI agent driven by GPT-4 from OpenAI, capable of autonomously training robots in intricate tasks. Eureka can independently craft reward algorithms and has successfully instructed robots in various activities, including pen-spinning tricks and opening drawers. It also published the Eureka library of AI algorithms, allowing experimentation with Nvidia Isaac Gym. This innovative work leverages the potential of LLMs and Nvidia's GPU-accelerated simulation technologies, marking a significant step in advancing reinforcement learning methods.   👉 OpenAI in Talks for $86 Billion Valuation, Surpassing Leading Tech Firms: OpenAI, the company responsible for ChatGPT, is reportedly in discussions to offer its employees' shares at an astounding $86 billion valuation, surpassing tech giants like Stripe and Shein. This tender offer is in negotiation with potential investors, although final terms remain unconfirmed. With Microsoft holding a 49% stake, OpenAI is on its way to achieving an annual revenue of $1 billion. If this valuation holds, it would place OpenAI among the ranks of SpaceX and ByteDance, becoming one of the most valuable privately held firms globally.  👉 Mojo SDK Now Available for Mac: Unleashing AI Power on Apple Silicon: The Mojo SDK, which has seen considerable success on Linux systems, is now accessible for Mac users, specifically Apple Silicon devices. This development comes in response to user feedback and demand. The blog post outlines the steps for Mac users to get started with the Mojo SDK. Additionally, there's a Visual Studio Code extension for Mojo, offering a seamless development experience. The Mojo SDK's remarkable speed and performance on Mac, taking full advantage of hardware capabilities, is highlighted. 👉 Qualcomm Reveals Snapdragon Elite X Chip for AI-Enhanced Laptops: Qualcomm introduced the Snapdragon Elite X chip for Windows laptops, optimized for AI tasks like email summarization and text generation. Google, Meta, and Microsoft plan to use these features in their devices, envisioning a new era of "AI PCs." Qualcomm aims to rival Apple's chips, claiming superior performance and energy efficiency. With the ability to handle AI models with 13 billion parameters, this chip appeals to creators and businesses seeking AI capabilities.  🔮 Expert Insights from Packt Community  Deep Learning with TensorFlow and Keras - Third Edition - By Amita Kapoor, Antonio Gulli, Sujit Pal Prediction using linear regression Linear regression is one of the most widely known modeling techniques. Existing for more than 200 years, it has been explored from almost all possible angles. Linear regression assumes a linear relationship between the input variable (X) and the output variable (Y). If we consider only one independent variable and one dependent variable, what we get is a simple linear regression. Consider the case of house price prediction, defined in the preceding section; the area of the house (A) is the independent variable, and the price (Y) of the house is the dependent variable.  We import the necessary modules. It is a simple example, so we’ll be using only NumPy, pandas, and Matplotlib: import tensorflow as tf import numpy as np import matplotlib.pyplot as plt import pandas as pd  Next, we generate random data with a linear relationship. To make it more realistic, we also add a random noise element. You can see the two variables (the cause, area, and the effect, price) follow a positive linear dependence: #Generate a random data np.random.seed(0) area = 2.5 * np.random.randn(100) + 25 price = 25 * area + 5 + np.random.randint(20,50, size = len(area)) data = np.array([area, price]) data = pd.DataFrame(data = data.T, columns=['area','price']) plt.scatter(data['area'], data['price']) plt.show() Now, we calculate the two regression coefficients using the equations we defined. You can see the result is very much near the linear relationship we have simulated: W = sum(price*(area-np.mean(area))) / sum((area-np.mean(area))**2) b = np.mean(price) - W*np.mean(area) print("The regression coefficients are", W,b)  ----------------------------------------------- The regression coefficients are 24.815544052284988 43.4989785533412 Let us now try predicting the new prices using the obtained weight and bias values: y_pred = W * area + b  Next, we plot the predicted prices along with the actual price. You can see that predicted prices follow a linear relationship with the area: plt.plot(area, y_pred, color='red',label="Predicted Price") plt.scatter(data['area'], data['price'], label="Training Data") plt.xlabel("Area") plt.ylabel("Price") plt.legend() This content is from the book “Deep Learning with TensorFlow and Keras - Third Edition” by Amita Kapoor, Antonio Gulli, Sujit Pal (Oct 2022). Start reading a free chapter or access the entire Packt digital library free for 7 days by signing up now. To learn more, click on the button below.Read through the Chapter 1 unlocked here...  🌟 Secret Knowledge: AI/LLM Resources📀 The Advantages of Small LLMs: Smaller LLMs are easier to debug and don't require specialized hardware, which is crucial in today's chip-demanding market. They are cost-effective to run, expanding their applicability. Additionally, they exhibit lower latency, making them suitable for low-latency environments and edge computing. Deploying small LLMs is more straightforward, and they can even be ensembled for improved performance. 📀 PyTorch Edge Unveils ExecuTorch for On-Device Inference: The PyTorch Edge team has introduced ExecuTorch, a solution that empowers on-device inference on mobile and edge devices with the support of industry leaders like Arm, Apple, and Qualcomm Innovation Center. ExecuTorch aims to address the fragmentation in the on-device AI ecosystem by offering extension points for third-party integration to accelerate ML models on specialized hardware.  📀 AI-Boosted Software Development Journey: AI assistance simplifies design, code generation, debugging, and impact analysis, streamlining workflows and enhancing productivity. From idea to production, this post takes you through various stages of development, starting with collaborative design sessions aided by AI tools like Gmail's help me write and Google Lens. Duet AI for Google Cloud assists in code generation, error handling, and even test case creation. This AI assistance extends to operations, service health monitoring, and security.  📀 Scaling Reinforcement Learning with Cloud TPUs: Learn how Cloud TPUs are revolutionizing Reinforcement Learning by enhancing the training process for AI agents. This article explores the significant impact of TPUs on RL workloads, using the DeepPCB case as an example. Thanks to TPUs, DeepPCB achieved a remarkable 235x boost in throughput and a 90% reduction in training costs, significantly improving the quality of PCB routings. The Sebulba architecture, optimized for TPUs, is presented as a scalable solution for RL systems, offering reduced communication overhead, high parallelization, and improved scalability.   💡 Masterclass: AI/LLM Tutorials🎯 Building an IoT Sensor Network with AWS IoT Core and Amazon DocumentDB: Learn how to create an IoT sensor network solution for processing IoT sensor data via AWS IoT Core and storing it using Amazon DocumentDB (with MongoDB compatibility). This guide explores the dynamic nature of IoT data, making Amazon DocumentDB an ideal choice due to its support for flexible schemas and scalability for JSON workloads.  🎯 Building Conversational AI with Generative AI for Enhanced Employee Productivity: Learn how to develop a lifelike conversational AI agent using Google Cloud's generative AI capabilities. This AI agent can significantly improve employee productivity by helping them quickly find relevant information from internal and external sources. Leveraging Dialogflow and Google enterprise search, you can create a conversational AI experience that understands employee queries and provides them with precise answers.  🎯 A Step-by-Step Guide to Utilizing Feast for Enhanced Product Recommendations: In this comprehensive guide, you will learn how to leverage Feast, a powerful ML feature store, to build effective product recommendation systems. Feast simplifies the storage, management, and serving of features for machine learning models, making it a valuable tool for organizations. This step-by-step tutorial will walk you through configuring Feast with BigQuery and Cloud Bigtable, generating features, ingesting data, and retrieving both offline and online features.  🎯 Constructing a Mini GPT-Style Model from Scratch: In this tutorial, you’ll explore model architecture, demonstrating training and inference processes. Know the essential components, such as data processing, vocabulary construction, and data transformation functions. Key concepts covered include tokens, vocabulary, text sequences, and vocabulary indices. The article also introduces the Self-Attention module, a crucial component of transformer-based models.  🎯 Deploy Embedding Models with Hugging Face Inference Endpoints: In contrast to LLMs, embedding models are smaller and faster for inference, which is valuable for updating models or improving fine-tuning. The post guides you through deploying open-source embedding models on Hugging Face Inference Endpoints. It also covers running large-scale batch requests. Learn about the benefits of Inference Endpoints, Text Embeddings Inference, and how to deploy models efficiently.  🚀 HackHub: Trending AI Tools🔨 xlang-ai/OpenAgents: Open platform with Data, Plugins, and Web Agents for data analysis, versatile tool integration, and web browsing, featuring a user-friendly chat interface. 🔨 AI-Citizen/SolidGPT: Technology business boosting framework allowing developers to interact with their code repository, ask code-related questions, and discuss requirements. 🔨 SkalskiP/SoM: Unofficial implementation of Set-of-Mark (SoM) tools. Developers can use it by running Google Colab to work with this implementation, load images, and label objects of interest.🔨 zjunlp/factchd: Code for detecting fact-conflicting hallucinations in text for developers to evaluate factuality within text produced by LLMs, aiding in the detection of factual errors and enhancing credibility in text generation. 

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!IntroductionLLM is the Large Language Model or the advanced artificial intelligence algorithms usually trained with vast amounts of text data. Such language models help to generate human-like languages. These models can also perform language-related tasks, including translation, text, competition, answering specific questions, and more.In this technological advancement era, several large language models are on the rise. Despite this, no standardized or fixed measures are used to compare or evaluate the quality of large language models.Here, let us dive into the existing evaluation and compare the framework for large language models. Also, we will analyze the factors on which these large language models should be evaluated.Evaluating Large Language ModelsNeed for a comprehensive evaluation framework Identifying different areas of improvement during the early developmental stages is relatively easy. However, with the advancement of technology and the availability of new alternatives, determining the best becomes increasingly tricky. Therefore, it is essential to have a reliable evaluation framework, helping to judge the quality of large language models accurately. Besides, the need for an immediate, authentic evaluation framework becomes imperative. One can use such a framework in the following ways.Only a proper framework will help the authorities and agencies to assess the accuracy, safety, usability issues, and reliability of the model.The blind race among the big technical companies to release large language models is on the rise. Hence, with the development of a comprehensive evaluation framework, one can help stakeholders to remove the model more responsibly.The comprehensive evaluation framework would help the user of large language models determine how and where to fine-tune the model to enable practical deployment.Issues with the existing framework  Every large language model has its advantages. However, certain factors are an issue and make the frameworks insufficient. Some of these issues includeSafety: Some of the framework does not consider protection a factor for evaluation. Although the open AI moderation API addresses safety to some extent, it is insufficient.Self-sufficiency: Regarding factors, one can evaluate the models; the frameworks are scattered. All of these frameworks need to be more comprehensive to be self-sufficient.Factors to be considered while evaluating large language modelsOnly after reviewing the existing evaluation framework can one determine the factors that must be considered while assessing the quality of large language models.Here are the key factors:Model Size and ComplexityThe primary factors to evaluate in LLMs are their size and complexity. It often gets indicated by the number of parameters. Generally, larger models have a greater capacity to understand context and generate nuanced responses. With the advent of huge models, one might require substantial computational resources, making them impractical for specific applications. Evaluators must balance model size and computational efficiency based on the use case.Training Data Quality and DiversityThe training data's quality and diversity significantly influence LLMs' performance. As users, we know that models get trained on diverse and representative datasets from various sources and tend to have a broader understanding of language nuances. However, evaluators should scrutinize the sources and types of data used for training to ensure the model's reliability across different contexts and domains.Bias and FairnessBias in LLMs is a critical concern, as it can generate discriminatory or unfair content. Evaluators must assess the model's bias, both in the training data and the generated output, and implement strategies to mitigate biases. Besides, ethical considerations demand continuous efforts to improve fairness, ensuring that the models do not reinforce societal biases.Ethical Considerations and Responsible UseEvaluating LLMs extends beyond technical aspects to ethical considerations. Responsible deployment of these models requires a thorough assessment of potential misuse scenarios. In every case, evaluators must devise guidelines and ethical frameworks to prevent generating harmful or malicious content, emphasizing the responsible use of LLMs in applications such as content moderation and chatbots.Fine-Tuning and Transfer Learning LLMs are often fine-tuned on specific datasets to adapt them to particular tasks or domains. One should scrutinize the fine-tuning process to ensure the model maintains its integrity and performance while being customized. Additionally, assessing the effectiveness of transfer learning, where models trained on one task are applied to related tasks, is crucial for understanding their adaptability and generalizability.Explainability and InterpretabilityUnderstanding how LLMs arrive at specific conclusions is essential, especially in applications like legal document analysis and decision-making processes. Being an evaluator, one must assess the model's explainability and interpretability. Transparent models enable users to trust the generated output and comprehend the reasoning behind the responses, fostering accountability and reliability.Robustness and Adversarial Attacks Evaluating the robustness of LLMs involves assessing their performance under various conditions, including noisy input, ambiguous queries, or adversarial attacks. Rigorous testing against potential negative inputs helps identify vulnerabilities and weaknesses in the model, guiding the implementation of robustness-enhancing techniques.Continuous Monitoring and ImprovementThe landscape of language understanding is ever-evolving. Continuous monitoring and improvement are vital aspects of evaluating LLMs. Regular updates, addressing emerging challenges, and incorporating user feedback contribute to the model's ongoing enhancement, ensuring its relevance and reliability over time.Step-by-Step Guide: Comparing LLMs Using Perplexity1. Load Language Model: Load the pre-trained LLM using a library like Hugging Face Transformers.2. Prepare Dataset: Tokenize and preprocess your dataset for the language model.3. Train/Test Split: Split the dataset into training and testing sets.4. Train LLM: Fine-tune the LLM on the training dataset.5. Calculate Perplexity: Use the testing dataset to calculate perplexity.Code example: # Calculate Perplexityfrom math import exp from transformers import GPT2LMHeadModel, GPT2Tokenizer tokenizer = GPT2Tokenizer.from_pretrained("gpt2") model = GPT2LMHeadModel.from_pretrained("gpt2") input_text = "Example input text for perplexity calculation." input_ids = tokenizer.encode(input_text, return_tensors="pt") with torch.no_grad():    output = model(input_ids)    loss = output.loss perplexity = exp(loss) print("Perplexity:", perplexity)Methods of evaluation Quantitative Performance Metrics and Benchmarking Evaluating LLMs requires rigorous quantitative assessment using industry-standard metrics. BLEU, METEOR, and ROUGE scores are pivotal in assessing text generation quality by comparing generated text with human references. For translation tasks, BLEU (Bilingual Evaluation Understudy) calculates the overlap of n-grams between the machine-generated text and human reference translations. METEOR evaluates precision, recall, and synonymy, providing a nuanced understanding of translation quality. ROUGE (Recall-Oriented Understudy for Gisting Evaluation) emphasizes summary evaluation, emphasizing memory. These metrics offer quantitative benchmarks, enabling direct comparison between different LLMs. Additionally, perplexity, a measure of how well a language model predicts a sample text, provides insights into language model efficiency. Lower perplexity values indicate better prediction accuracy, highlighting the model's coherence and understanding of the input text. Often applied to large-scale datasets like WMT (Workshop on Machine Translation) or COCO (Common Objects in Context), these quantitative metrics, off LLM, are a robust foundation for comparing LLMs' performance.Diversity Analysis and Bias Detection Diversity and bias analysis are paramount in evaluating LLMs, ensuring equitable and inclusive performance across diverse demographics and contexts. One critical approach involves employing word embedding techniques, such as Word Embedding Association Test (WEAT), to quantify biases. WEAT assesses associations between word embeddings and predefined categories, unveiling tendencies present in LLMs. By evaluating gender, race, or cultural preferences, organizations can ensure fair and unbiased responses, aligning with ethical considerations.Furthermore, demographic diversity analysis measures the model's performance across different demographic groups. Assessing demographic parity ensures that LLMs provide consistent, unbiased results across various user segments. This comprehensive evaluation approach, deeply rooted in fairness and inclusivity, is pivotal in selecting socially responsible LLMs.Real-World User Studies and Interaction AnalysisIncorporating real-world user studies and interaction analysis is indispensable for evaluating LLMs in practical scenarios. Conducting user tests and surveys provides qualitative insights into user satisfaction, comprehension, and trust. These studies consider how well LLM-generated content aligns with users' expectations and domain-specific contexts.Additionally, analyzing user interactions with LLM-generated content through techniques like eye-tracking studies and click-through rates provides valuable behavioral data. Heatmap analysis, capturing user attention patterns, offers insights into the effectiveness of LLM-generated text elements. User feedback and interaction analysis inform iterative improvements, ensuring that LLMs are technically robust, user-centric, and aligned with real-world application requirements.ConclusionWith the development of large language models, natural language processing experienced a revolution. However, the need for a standardized and comprehensive evaluation framework remains a necessity. It helps in assessing the quality of these LLM models. Though the existing framework offers valuable insights, it needs more standardization and comprehensiveness. At the same time, it does not consider safety as an evaluation factor. Moreover, collaborating with relevant experience becomes imperative to build a comprehensive and authentic evaluation framework for the large language models.Author BioVivekanandan, a seasoned Data Specialist with over a decade of expertise in Data Science and Big Data, excels in intricate projects spanning diverse domains. Proficient in cloud analytics and data warehouses, he holds degrees in Industrial Engineering, Big Data Analytics from IIM Bangalore, and Data Science from Eastern University.As a Certified SAFe Product Manager and Practitioner, Vivekanandan ranks in the top 1 percentile on Kaggle globally. Beyond corporate excellence, he shares his knowledge as a Data Science guest faculty and advisor for educational institutes.

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!IntroductionAzure machine learning prompt flow is one of Microsoft's cutting-edge solutions. It has paved the path for handling streamlined data. Thus enabling all the data scientists to focus on driving valuable insights from data.At the heart of this innovation lies the vector data store. It is one of the powerful tools that ensures seamless data manipulation and integration.Let us delve deeply into the depths of vector data storage while exploring its functionality and significance in Azure machine learning prompt flow.Understanding Vector datastoreA vector datastore can handle large-scale vectorized data efficiently within the Azure Machine Learning ecosystem. It acts as one of the centralized repositories that houses diverse data formats, from texts to images and numerical data. The real power of the vector data store lies in the ability to unify such disparate data types into a cohesive format that helps other data scientists work seamlessly.Some of the key benefits and features of vector data storing in the ecosystem of Azure ML include:Data integrationWith the help of a vector datastore, data scientists can integrate a variety of data types without going through the hassles of format conversion. Due to this hassle, the system accelerates the data preprocessing phase, which is one of the crucial steps in any Machine Learning project.Efficient data manipulationVector datastore makes complex operations like filtering, feature extraction, quality confirmation and transformation the most straightforward process. This efficient data manipulation technique is crucial to derive meaningful patterns from the raw data. Such a process leads to more accurate machine-learning models.Scalability The vector datastore of Azure Machine learning prompt flow helps to scale effortlessly. As a result, it helps in accommodating the growing datasets. Whether the user deals with petabytes or gigabytes of data, a vector datastore ensures smooth operations without compromising the accuracy and speed of the whole process.Version controlVector database makes data versioning simplified. It allows the data scientists to keep track of the changes, reproduce experiments with precision, and collaborate effectively.Let's consider a scenario where we want to preprocess a dataset containing images of handwritten digits for a digit recognition task. First, we'll initialize a Vector Datastore to store our image data.import numpy as np import cv2 # Define a function to load and preprocess images def load_images(file_paths, target_size=(64, 64)):    images = []    for file_path in file_paths:        # Read the image using OpenCV        image = cv2.imread(file_path)        # Resize the image to the target size        image = cv2.resize(image, target_size)        # Normalize pixel values to be between 0 and 1        image = image.astype('float32') / 255.0        images.append(image)    return np.array(images) # Example usage file_paths = ['image1.jpg', 'image2.jpg', 'image3.jpg']  # List of file paths to your images image_data = load_images(file_paths) # Now image_data contains the preprocessed image data ready to be stored in your vector datastoreIn this example, the code snippet demonstrates how to initialize a Vector Datastore, upload the image dataset, and create a dataset from the stored images.Output:Upon successful execution, the image dataset is seamlessly integrated into the Vector Datastore, ready for preprocessing and model training. This integration ensures that data scientists can focus on building robust machine learning models without worrying about data compatibility issues.Creating Vector indexVector index lookup is a tailored tool that helps to make queries within Azure Machine Learning Vector datastore index. It helps to empower the user to extract relevant information related to the context from a domain knowledge base.Here is how one can prepare one's own Data QnA by placing a vector index as an input. However, on the basis of the place where you put the vector index, the identity used by Azure ML promptflow gets granted certain roles.Inputs:After installing Annoy library, you can createa vector index using the below code.from annoy import AnnoyIndex # Assuming image_data is your preprocessed image data (NumPy array) # Each row in image_data represents a flattened image vector # Define the number of dimensions in your vectors (usually the length of each flattened image vector) num_dimensions = len(image_data[0]) # Initialize Annoy index with the number of dimensions in your vectors annoy_index = AnnoyIndex(num_dimensions) # Add vectors to the index for i, vector in enumerate(image_data):    annoy_index.add_item(i, vector) # Build the index for efficient nearest neighbor searches annoy_index.build(n_trees=10)  # You can adjust the number of trees for optimization # Now, the annoy_index is ready for efficient nearest neighbor searches # You can perform queries using annoy_index.get_nns_by_vector(vector, num_neighbors, search_k) # For example: # nearest_neighbors = annoy_index.get_nns_by_vector(query_vector, num_neighbors=5, search_k=-1)Outputs:The index will be initialized with various dimensions in the image vectors.Choosing a vector storeOne must use a vector index to perform Retrieval Augmented Generation in Azure Machine learning. It helps store the embeddings, which can later be converted to number sequences. Such an elaborate process helps the large language models to understand any complex relation between those concepts. Only if the user can create vector stores will it help them to hook up the data with a large language model, including GPT4. At the same time, one can also retrieve the data efficiently.Azure Machine Learning prompt flow usually supports two kinds of vector stores in the RAG workflow.FaissIt is one of the open-source libraries that provides the user with a local file-based store. One can find the vector index stored in the storage account of the Azure machine learning workspace. Since the storage system is locally stored, the costs are also minimal. Hence, the whole process of testing and development is budget-friendly.Azure Cognitive SearchIt is one of the Azure resources that supports information retrieval over the textual data and vector stored in search retrieval. With the help of prompt flow, one can populate, create, and query the vector data stored in Azure cognitive search.Though you can choose any of the vectors mentioned above stores, here is an overview of which should be used.Faiss, an open-source library, emerges as a robust solution, particularly when dealing with vector-only data. It stands as an essential component that can be seamlessly integrated into your solution. Let's explore the key aspects of Faiss, coupled with the capabilities of Azure Cognitive Search, to understand how these tools can be harnessed effectively.Faiss: Optimizing Vector Data ManagementFaiss offers several compelling advantages when it comes to working with vector data:1. Cost-Effective Local Storage: Faiss allows local storage without incurring additional costs for creating an index, offering a budget-friendly option for businesses aiming to optimize their expenses while managing extensive datasets.2. In-Memory Indexing and Querying: One of Faiss' standout features is its ability to build and query indexes entirely in memory. This approach significantly enhances the speed of operations, making it an efficient choice for real-time applications.3. Flexibility in Sharing: Faiss enables the sharing of index copies for individual use, providing flexibility in data access. However, additional setup is necessary for applications requiring index hosting to ensure tailored solutions for diverse use cases.4. Scalability Aligned with Computational Resources: Faiss scales seamlessly with the underlying compute resources, enabling businesses to manage varying workloads effectively. Its ability to adapt to the computational load ensures consistent performance despite fluctuating demands.Example:Consider an e-commerce platform dealing with millions of product vectors. By utilizing Faiss, the platform can create an in-memory index, enabling lightning-fast similarity searches for product recommendations, enhancing user experience, and increasing sales.Azure Cognitive Search: Elevating Vector Data Management to Enterprise Level Azure Cognitive Search, a dedicated Platform as a Service (PaaS) resource, offers a comprehensive solution for businesses seeking robust vector data management:1. Enterprise-Grade Scalability and Security: Cognitive Search supports enterprise-level business requirements, offering scalability, security, and availability. It ensures seamless scaling to accommodate growing data volumes, such an attribute makes it an ideal choice for businesses of all sizes.2. Hybrid Information Retrieval: A unique feature of Cognitive Search is its ability to support hybrid information retrieval. It means that vector data can coexist harmoniously with non-vector data. Businesses can leverage all the features of Azure Cognitive Search, including hybrid search and semantic reranking, ensuring comprehensive data analysis.3. Vector Support in Public Preview: Cognitive Search's vector support is currently in public preview. Although vectors must be generated externally, Cognitive Search handles the indexing and query encoding seamlessly within the prompt flow, simplifying the integration process.Example:Consider a financial institution needing to process massive amounts of transaction data, including structured and vector data, for fraud detection. Azure Cognitive Search allows seamless integration of vector data, enabling the institution to identify patterns effectively and enhance security protocols.Integration for Seamless Vector Data ManagementTo utilize Cognitive Search as a vector store for Azure Machine Learning, you must establish a search service within your Azure subscription. Once the service is in place, developers can access it. Azure Cognitive Search can be chosen as a vector index within the prompt flow. The prompt flow facilitates the entire process, from index creation to vector generation, ensuring a streamlined experience.The synergy between Faiss and Azure Cognitive Search presents a formidable solution for businesses aiming to manage vector data effectively. Faiss' efficiency in local storage and real-time querying, coupled with Cognitive Search's enterprise-grade scalability and hybrid data support, creates a powerful ecosystem. This integration empowers businesses to leverage their vector data fully, facilitating data-driven decision-making and driving innovation in diverse industries.By harnessing the capabilities of Faiss and Azure Cognitive Search, companies can truly unlock the potential of their data, paving the way for a future where data management is as intelligent as the insights derived from it.ConclusionVector datastore accelerates the machine learning pipelines, leading to faster innovations and more accurate models. As organizations continue to grapple with massive data sets, the only solution that can enhance accuracy and efficiency becomes indispensable. Hence, vector datastore in Azure machine learning promptflow is not a choice but a necessity. It unifies the diverse data types, coupled with scalability and efficient manipulation, enabling the data scientist to extract valuable insights, especially from complex and large data sets.Author BioKarthik Narayanan Venkatesh (aka Kaptain), founder of WisdomSchema, has multifaceted experience in the data analytics arena. He has been associated with the data analytics domain since the early 2000s, with a ringside view of transformations in this industry. He has led teams that architected and built scalable data platform solutions across the technology spectrum.As a niche consulting provider, he bridged the gap between business and technology and drove BI adoption through innovative approaches in an agnostic manner. He is a sought-after speaker who has presented many lectures on SAP, Analytics, Snowflake, AWS, and GCP technologies.