Databricks for Practitioners : The AI Lakehouse Playbook

The complete production AI playbook for Databricks.RAG. Agent Bricks. Multi-agent supervisors. Lakebase. Feature Store. MLflow 3. Lakehouse Monitoring. Foundation Model APIs. Vector Search. AI Gateway. Every AI surface Databricks shipped at GA in 2025-2026, taught by a senior practitioner, current to 2026.Key Features:• RAG end-to-end with Vector Search, embedding models, hybrid retrieval, and citation grounding• Agent Bricks + Multi-Agent Supervisor + MCP with the eval loop that catches routing failures before production• Lakebase — operational Postgres at sub-10ms reads, the layer that turns AI models and agents into user-facing apps• Full ML lifecycle — Feature Store, MLflow 3, Lakehouse Monitoring, AI GatewayBook DescriptionAI on Databricks went from research project to production platform in 2025-2026. Foundation Model APIs, Vector Search, Agent Bricks, the Multi-Agent Supervisor, MLflow 3, Lakehouse Monitoring, and Lakebase all shipped at GA. Volume 4 is the book that uses them together.It walks the full Mosaic AI surface. Model Serving for production inference. Foundation Model APIs for prebuilt frontier models. Vector Search and Retrieval-Augmented Generation for grounding AI in your own data. MLflow 3 and the UC Model Registry for the experiment-to-production lifecycle. Feature Store for features that bridge offline training and online serving.The agentic chapters: Agent Bricks ships declarative single-agent systems (e.g., classification, information extraction) that require no prompts. The Multi-Agent Supervisor orchestrates specialists — Genie spaces, custom agents, Agent Brick children, and external tools via the Model Context Protocol, with an eval loop that catches incorrect routing, tool hallucination, and unbounded recursion before production.Lakebase, the operational Postgres shipped with Databricks in 2026, gives AI apps the sub-ten-millisecond reads they need to serve users, the layer that turns models and agents into real products. Lakehouse Monitoring catches the quiet drift that destroys ML systems while every dashboard stays green. The Chapter 54 capstone wires Lakebase, the agents, and the analytics into a full retail intelligence app. Azure examples; concepts apply to AWS and GCP.What you will learn:• Build RAG pipelines with Vector Search, embedding models, and citation grounding• Ship Agent Bricks for classification and information extraction• Orchestrate specialist agents with the Multi-Agent Supervisor and MCP• Use Lakebase as the operational Postgres layer for AI apps and agents• Detect data and model drift with Lakehouse Monitoring; wire alerts to retraining• Manage the ML lifecycle with MLflow 3 and the UC Model Registry• Govern features across training and serving with Feature Store (offline + online)• Serve foundation and custom models with AI Gateway controls• Certification preparationWho this book is for:Data engineers, ML engineers, and AI/ML architects who know PySpark and the Databricks platform and now need to ship production AI.Table of Contents1. Databricks SQL in Production2. External BI: Tableau, Power BI, dbt3. AI/BI Dashboards4. Genie: Natural-Language Analytics5. AI SQL Functions6. Model Serving7. Foundation Models8. Vector Search and RAG9. MLflow 3 and UC Model Registry10. Feature Store11. MLOps as a Practice12. Lakehouse Monitoring: Drift Detection13. Distributed Deep Learning14. Agent Bricks15. Multi-Agent Supervisor and MCP16. Lakebase: Operational Postgres for AI17. Capstone: Retail Intelligence App18. Certification and What's Next Read more