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The Databricks AI Bible: Building, Governing, and Scaling Enterprise GenAI with Mosaic AI

A living book — updated weekly — on LLMOps, Agent Bricks, Vector Search, and Databricks Apps

Diego Poggioli

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About

About

About the Book

The Databricks AI ecosystem moves fast. Most books are outdated before they ship.

This one isn't.

The Databricks AI Bible is a living technical guide for Solution Architects designing enterprise GenAI systems on Databricks and Azure. It covers the entire Mosaic AI suite — from Delta tables optimized for AI workloads, to Vector Search indexes, AI Functions in SQL, LLMOps with MLflow, Mosaic AI Model Serving and Gateway, Agent Bricks with MCP tool integration, Databricks Apps, and AI/BI Genie Spaces.

Every chapter follows a production-first template: Problem → Architecture Pattern → Implementation → Production Checklist.

The book is updated daily by a research agent that monitors Databricks release notes, Azure AI updates, and the Data+AI Summit feed. When Databricks ships a feature, this book reflects it within 48 hours.

Buy once. Get all future updates for free.

**By D. Poggioli** — enterprise AI architect and Azure + Databricks specialist.

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About the Author

Diego Poggioli

Contents

Table of Contents

Chapter 1: Why Databricks for Enterprise GenAI in 2026

  1. The convergence of data, AI, and governance has a winner — and it isn’t what most teams chose three years ago.
  2. Learning Objectives
  3. The Problem
  4. Why This Matters in 2026
  5. Core Concepts
  6. Architecture Pattern
  7. Implementation
  8. Production Checklist
  9. Common Pitfalls
  10. Exercises
  11. Further Reading
  12. Chapter Summary
  13. What’s Next

Chapter 2: The Databricks Lakehouse Architecture — From Data to AI

  1. Before you build AI systems, you must understand where the data lives and who controls it.
  2. Learning Objectives
  3. The Problem
  4. Why This Matters in 2026
  5. Core Concepts
  6. Architecture Pattern
  7. Implementation
  8. Production Checklist
  9. Edge Cases and Gotchas
  10. Summary
  11. Further Reading
  12. Chapter Summary
  13. What’s Next

Chapter 3: Delta Tables for AI Workloads

  1. Advanced Optimization for AI Search, Feature Retrieval, and Training Data
  2. Learning Objectives
  3. The Problem
  4. Why This Matters in 2026
  5. Core Concepts
  6. Architecture Pattern
  7. Implementation
  8. Production Checklist
  9. Edge Cases and Gotchas
  10. Summary
  11. Further Reading
  12. Chapter Summary
  13. What’s Next

Chapter 4: Unity Catalog for AI

  1. Governing Models, Vectors, and Agent Credentials in One Control Plane
  2. Learning Objectives
  3. The Problem
  4. Why This Matters in 2026
  5. Core Concepts
  6. Architecture Pattern
  7. Implementation
  8. Production Checklist
  9. Edge Cases and Gotchas
  10. Summary
  11. Further Reading
  12. Chapter Summary
  13. What’s Next

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