[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8},"actionData","errors","root",{"_28":-5,"_38":-5,"_39":40,"_41":42,"_43":44,"_45":46,"_47":48,"_49":50,"_51":52,"_53":54,"_55":56,"_57":58},"routes/_store+/_readers+/email_author.$slug/route",{"_9":10,"_11":-5},"book",{"_12":13,"_14":15,"_16":17,"_18":19,"_20":21,"_22":23},"currentUser","id","Z2lkOi8vbGVhbnB1Yi9TdG9yZTo6Qm9va1Byb2R1Y3QvNzk2MjM","title","Databricks for Practitioners: Volume 2","state","published","contactAuthorMethod","email","aboutText","

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 Gateway

AI 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 are the center piece. Agent Bricks ships declarative single-agent systems (classification, information extraction) without writing prompts. The Multi-Agent Supervisor orchestrates specialists, Genie spaces, custom agents, Agent Brick children, external tools via the Model Context Protocol, with the eval loop that catches wrong 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 capstone wires Lakebase, the agents, and the analytics into a

full retail intelligence app. Azure examples; concepts apply on AWS and GCP.

","authors",[24],{"_12":25,"_26":27,"_28":29},"Z2lkOi8vbGVhbnB1Yi9TdG9yZTo6UHJvZHVjdEF1dGhvci84NTE5NA","name","Ritesh Modi","user",{"_30":31},"avatar",{"_32":33,"_34":35,"_36":37},"url","https://d39qdlcrvnra4b.cloudfront.net/avatars/3714687/medium/avatar.jpg?1777217214","styleName","medium","exists",true,"shoppingCartId","shoppingCart",{"_12":71,"_72":73,"_74":37,"_75":76,"_77":78,"_79":80,"_81":37},"locale","en","readerTheme","light","constants",{"_69":70},"publishers",{"_65":66,"_67":68},"learnerOrganizations",[],"needsLocaleCookieSync",false,"announcements",{"_59":60,"_61":-5},"sentryEnvironment","production","sentryRelease","f5548b0fa7c03e0c3ddcc40793859fac31237ca7","announcement_store",{"_62":52,"_63":64},"announcement_authors","flag","text","

Kick off your book project, get started with GhostAI, get better at marketing, or spend the day doing all three! Free live workshops on Zoom. Saturday, June 27, 2026.

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