Databricks ML in Action

Dive into the Databricks Data Intelligence Platform and learn how to harness its full potential for creating, deploying, and maintaining machine learning solutions. This book covers everything from setting up your workspace to integrating state-of-the-art tools such as AutoML and VectorSearch, imparting practical skills through detailed examples and code.

What this Book will help me do

• Set up and manage a Databricks workspace tailored for effective data science workflows.
• Implement monitoring to ensure data quality and detect drift efficiently.
• Build, fine-tune, and deploy machine learning models seamlessly using Databricks tools.
• Operationalize AI projects including feature engineering, data pipelines, and workflows on the Databricks Lakehouse architecture.
• Leverage integrations with popular tools like OpenAI's ChatGPT to expand your AI project capabilities.

Author(s)

This book is authored by Stephanie Rivera, Anastasia Prokaieva, Amanda Baker, and Hayley Horn, seasoned experts in data science and machine learning from Databricks. Their collective years of expertise in big data and AI technologies ensure a rich and insightful perspective. Through their work, they strive to make complex concepts accessible and actionable.

Who is it for?

This book serves as an ideal guide for machine learning engineers, data scientists, and technically inclined managers. It's well-suited for those transitioning to the Databricks environment or seeking to deepen their Databricks-based machine learning implementation skills. Whether you're an ambitious beginner or an experienced professional, this book provides clear pathways to success.