In today's race to harness generative AI, many teams struggle to integrate these advanced tools into their business systems. While platforms like GPT-4 and Google's Gemini are powerful, they aren't always tailored to specific business needs. This book offers a practical guide to building scalable, customized AI solutions using the full potential of data lakehouse architecture.

Author Bennie Haelen covers everything from deploying ML and GenAI models in Databricks to optimizing performance with best practices. In this must-read for data professionals, you'll gain the tools to unlock the power of large language models (LLMs) by seamlessly combining data engineering and data science to create impactful solutions.

• Learn to build, deploy, and monitor ML and GenAI models on a data lakehouse architecture using Databricks
• Leverage LLMs to extract deeper, actionable insights from your business data residing in lakehouses
• Discover how to integrate traditional ML and GenAI models for customized, scalable solutions
• Utilize open source models to control costs while maintaining model performance and efficiency
• Implement best practices for optimizing ML and GenAI models within the Databricks platform

Table of Contents

Chapter 1. An Overview of Machine Learning, Al, and GenAI

Chapter 2. The Databricks Data Intelligence Platform

Chapter 3. An Introduction to ML on Databricks

Chapter 4. End-to-End ML with MLflow

Chapter 5. Feature Engineering in the Unity Catalog

Chapter 6. ML at Scale

Chapter 7. GenAI in the Lakehouse: Foundations and Architecture

Chapter 8. GenAI in a Databricks Lakehouse Environment

Chapter 9. Al Agents in the Lakehouse

Chapter 10. The Model Context Protocol

Chapter 11. Agent-to-Agent Communication and the DSPy Framework

About the Author

Bennie Haelen is a principal architect with Insight, a Microsoft and Databricks partner. As principal architect, his primary focus areas are modern data warehousing, machine learning, and generative AI on various commercial cloud platforms. Over his career, Bennie has overseen many data & AI projects in different application domains, such as healthcare, energy, and financial applications. He has also architected and delivered generative AI solutions that leverage both commercial and open source models.