Practical Machine Learning on Databricks: Seamlessly transition ML models and MLOps on Databricks
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Take your machine learning skills to the next level by mastering databricks and building robust ML pipeline solutions for future ML innovations
Key FeaturesLearn to build robust ML pipeline solutions for databricks transitionMaster commonly available features like AutoML and MLflowLeverage data governance and model deployment using MLflow model registryPurchase of the print or Kindle book includes a free PDF eBookBook 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.
What you will learnTransition smoothly from DIY setups to databricksMaster AutoML for quick ML experiment setupAutomate model retraining and deploymentLeverage databricks feature store for data prepUse MLflow for effective experiment trackingGain practical insights for scalable ML solutionsFind out how to handle model drifts in production environmentsWho this book is 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.
Table of ContentsML Process and ChallengesOverview of ML on DatabricksUtilizing Feature Store Understanding MLflow ComponentsCreate a Baseline Model for Bank Customer Churn Prediction Using AutoMLModel Versioning and WebhooksModel Deployment ApproachesAutomating ML Workflows Using the Databricks JobsModel Drift Detection for Our Churn Prediction Model and RetrainingCI/CD to Automate Model Retraining and Re-Deployment.
Publisher : Packt Publishing (November 24, 2023)
Language : English
Paperback : 244 pages
ISBN-10 : 1801812039
ISBN-13 : 978-1801812030
Item Weight : 15.1 ounces
Dimensions : 9.25 x 7.52 x 0.52 inches
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8 reviews for Practical Machine Learning on Databricks: Seamlessly transition ML models and MLOps on Databricks
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Arjun –
Fantastic
Excellent book for anyone interested in clearing the ML professional certification. Very well structured, great explanations followed up by clear examples!
AMEY –
Get practical with Databricks!
ð 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.ð¡ 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.ð 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.ð 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.
Advitya Gemawat –
`Databricks++`
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 membersâitâs 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!ð 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!
Dev –
Great Book on for ML practitioners looking to get started on Databricks
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
tt0507 –
Great resource for Databricks
Practical Machine Learning on Databricks is a great resource for data scientists, developers, and ML engineers who want to use Databricks in their day-to-day workflow. The book focuses more on describing MLOps features on Databricks instead of model generation which may be great for ML engineers. The book provides extensive guides for ML features such as AutoML, Feature Store, and MLflow, which makes the book a perfect resource as these features are also supported by other MLOps services. Overall, I recommend this book due to its popularity of Databricks and extensive applications.
H2N –
Good ML book using Databricks
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.
Jean –
You already have all the code and information in Databricks docs
Jennifer Owen –
Content is really useful, but when I try to download the pdf it says that the book hasnât been published yet?