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.
publication date
2023-11-24
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
key features
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
approach
This book offers a hands-on approach to mastering Databricks for ML projects. It starts with an overview of Databricks and MLflow, followed by actionable guidance on data prep, model selection, and feature engineering. Code examples illustrate each phase, from AutoML experimentation to deployment and monitoring. The content focuses on generally available features but prepares you for future ML innovations.
audience
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.
short description
Learn key databricks features like AutoML and MLflow and deploy serverless models with ease with this comprehensive guide. The book helps you build a strong foundation and sets you up to adapt easily to future ML innovations.
subtitle
Seamlessly transition ML models and MLOps on Databricks
keywords
Data science, Data science books, Databricks book, Databricks python
Product ISBN
9781801812030
