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Key benefits

Learn to build robust ML pipeline solutions for databricks transition

Master commonly available features like AutoML and MLflow

Leverage data governance and model deployment using MLflow model registry

Purchase of the print or Kindle book includes a free PDF eBook

Description

Unleash the potential of databricks for end-to-end machine learning with this comprehensive guide, tailored for experienced data scientists and developers transitioning from DIY or other cloud platforms. Building on a strong foundation in Python, Practical Machine Learning on Databricks serves as your roadmap from development to production, covering all intermediary steps using the databricks platform. You’ll start with an overview of machine learning applications, databricks platform features, and MLflow. Next, you’ll dive into data preparation, model selection, and training essentials and discover the power of databricks feature store for precomputing feature tables. You’ll also learn to kickstart your projects using databricks AutoML and automate retraining and deployment through databricks workflows. By the end of this book, you’ll have mastered MLflow for experiment tracking, collaboration, and advanced use cases like model interpretability and governance. The book is enriched with hands-on example code at every step. While primarily focused on generally available features, the book equips you to easily adapt to future innovations in machine learning, databricks, and MLflow.

Who is this book for?

This book is for experienced data scientists, engineers, and developers proficient in Python, statistics, and ML lifecycle looking to transition to databricks from DIY clouds. Introductory Spark knowledge is a must to make the most out of this book, however, end-to-end ML workflows will be covered. If you aim to accelerate your machine learning workflows and deploy scalable, robust solutions, this book is an indispensable resource.

What you will learn

Transition smoothly from DIY setups to databricks

Master AutoML for quick ML experiment setup

Automate model retraining and deployment

Leverage databricks feature store for data prep

Use MLflow for effective experiment tracking

Gain practical insights for scalable ML solutions

Find out how to handle model drifts in production environments

What do you get with Print?

Instant access to your digital copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Redeem a companion digital copy on all Print orders

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

Practical Machine Learning on Databricks

€33.99

€37.99

Machine Learning Engineering with Python

€37.99

Total € 109.97

Advitya Gemawat Feb 27, 2024

More than just a seamless transition to Databricks, the book covers several specific examples and capabilities to supercharge ML development with Databricks:1. AutoML: Quick Experiment SetupOne principle I learnt from a professor in college is that a one-size solution never fits all in Machine Learning. Using AutoML capabilities such as Databricks AutoML, however, can be an easier enterprise-grade approach to quickly get started and establish a baseline.2. MLflow: Effective Experiment TrackingThe book provides practical insights on using MLFlow, an industry standard at this point for experiment tracking, versioning, and reproducibility. The step-by-step guide on setting up MLflow tracking servers and integrating them with Databricks notebooks is invaluable. Imagine having a centralized dashboard to monitor your experiments, compare models, and collaborate with team members—it’s a productivity boost!3. Model Deployment & Versioning ApproachesFrom webhooks to real-time predictions with Databricks Jobs, there's actionable knowledge to put models into production. Rolling back to a previous model version during production incidents is critical to ensure reliability.4. Handling Model & Data DriftsModel & Data drifts are inevitable in the real world. The strategies for monitoring & mitigating drifts using Databricks Delta Lake and triggering retraining pipelines initially seems eye-opening. Model performance doesn't just need to be achieved but maintained!🔍 But wait, there's more! :Feature Store: Yep, Databricks has its own to create reusable features, manage feature versions, and integrate them seamlessly into ML pipelines.CI/CD Automation: Automate ML workflows using Databricks Jobs. Version-controlled notebooks, automated testing, and continuous integration empower teams to iterate faster.Azure Databricks: Getting started with Databricks doesn't mean migrating to a new platform. Databricks integrates with other cloud platforms, and Azure Databricks (especially coupled with the latest Microsoft Fabric capabilities) can be the perfect fit!

Amazon Verified review

H2N Dec 14, 2023

Practical Machine Learning on Databricks is a great resource for data scientists and developers who want to use Databricks in machine learning in Python language . It guides readers with MLflow from data preparation, model selection, training to model employment with different hands on examples. The author gives helpful instruction with Databricks AutoML for efficient project management and collaboration techniques. An nice tool in Databricks-driven machine learning projects.

Amazon Verified review

S.Kundu Apr 24, 2024

Machine learning on Databricks accelerates insights with scalable processing. Its unified analytics platform seamlessly integrates data science workflows, leveraging distributed computing. With collaborative features, teams streamline model development and deployment. Databricks empowers experimentation with robust libraries and MLflow integration for efficient model tracking. From data ingestion to model deployment, Databricks simplifies the end-to-end machine learning lifecycle.Practical Machine Learning on Databricks is designed for developers and data scientists who want to leverage Databricks to work on machine learning projects. I have been looking for a book which can help me in implementing end-to-end machine learning projects in Databricks and this book definitely ended my search. This book assumes that you have knowledge in Python, statistics, machine learning cycles along with understanding of Spark. Although it is only around 200 pages book but it explains each and every concept practically starting from providing the overview till the deployment as a CICD pipeline.A few important topics of the book that I want to highlight are as below:The First part of the book starts with ML Process and its Challenges along with exploring Databricks and the Lakehouse architectureThe book will explain what is a Feature store and will help you in registering your feature table in Databricks Feature StoreThen it will help you to understand MLFlow Components on Databricks such as MLFlow Tracking, MLFlow Models and MLFlow Model Registry along with creation of Baseline Model using Databricks AutoMLThe book will cover the process of registering your model to Model Registry and deep dive into webhooks support in the Model Registry. It will also explain ML deployments and paradigms along with how you can deploy ML models for batch, streaming and real-time inferenceIt will then cover topics of how you can automate ML Workflows using Databricks Jobs to automate model training and testing.The final part covers concept of Model Drift and different techniques for drift detection along with implementation of drift detection on Databricks. It will also introduce you to MLOps and how you can deploy using different deployment patterns such as deploy models approach and deploy code approach

Amazon Verified review

AMEY Mar 04, 2024

🌟 OverviewThis book meticulously navigates through the lifecycle of ML projects, addressing the foundational processes, challenges, and the pivotal role of platforms like Databricks in facilitating scalable, secure, and efficient ML solutions. Sinha does an exceptional job of distilling complex concepts into understandable segments, making it accessible to both novices and experienced practitioners.💡 Key HighlightsComprehensive Coverage: From the basics of ML processes to advanced topics like ML governance and deployment, the book covers a broad spectrum. The detailed chapters on Databricks' Lakehouse architecture and ML pipeline components are particularly enlightening.Practical Insights: The inclusion of real-world examples, technical requirements for different stages of ML projects, and hands-on guides on utilizing Databricks features like Feature Store, MLflow, and AutoML enriches the learning experience.Enterprise Focus: Understanding the intricacies of productionizing ML in an enterprise context is a significant challenge, and Sinha adeptly addresses this. The book delves into scalability, performance, security, and governance, providing a roadmap for deploying enterprise-grade ML platforms.Future-Ready Learning: With sections dedicated to automation, model drift detection, and retraining, the book equips readers to tackle future challenges in ML deployment, emphasizing continuous learning and adaptability.🚀 My Takeaway"Practical Machine Learning on Databricks" is more than just a technical manual; it's a strategic guide for effectively implementing ML projects. Sinha's clear writing, combined with practical examples and strategic insights, offers invaluable knowledge to anyone looking to harness the power of Databricks for ML projects.Whether you're a data scientist, ML engineer, or a business leader looking to leverage ML, this book is a must-read. It not only educates but also inspires innovation and efficiency in ML projects.📚 RecommendationI highly recommend "Practical Machine Learning on Databricks" to anyone in the field of data science and machine learning. It's a resource that you'll find yourself returning to, whether as a reference guide in your professional projects or as a learning tool to stay abreast of the latest in ML deployment.

Amazon Verified review

Dev Jan 24, 2024

This book gives a great overview of ML features on Databricks and helped me understand the parallels between DIY MLOps and how my model pipelines will look in Production. The code examples are also easy to understand. I recently cleared the Databricks ML professional certification. This book acted as a great guide! thanks

Amazon Verified review

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Practical Machine Learning on Databricks: Seamlessly transition ML models and MLOps on Databricks

What do you get with Print?