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Databricks for Practitioners: Volume 2
The AI Lakehouse and Agentic Playbook: Analytics, Mosaic AI, Agents, and Lakebase
RAG, Agent Bricks, the Multi-Agent Supervisor with MCP, Lakebase, MLflow 3, Lakehouse Monitoring, Feature Store, Vector Search. Every AI surface Databricks shipped at GA in 2025 and 2026, taught by a practitioner, current to 2026.
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
Who 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. Volume 3 is the recommended prerequisite.
Table of Contents
1. Databricks SQL in Production. Warehouses, materialized views, three latency signals (admission, compilation, execution), the full dashboard backend wiring.
2. External BI: Tableau, Power BI, dbt. Performance tips that take a dashboard from sluggish to instant, dbt configuration at incremental scale, the seam between BI and the lakehouse.
3. AI/BI Dashboards. Anatomy of a Lakeview dashboard, draft vs published flow, the Dashboard Agent's reliable patterns, the five-grant permission model.
4. Genie: Natural-Language Analytics. Grounding sources, the priority rule, the SQL Genie actually writes, the questions Genie answers cleanly versus the ones that confuse it.
5. AI SQL Functions. ai_query, ai_parse_document, ai_extract for PDFs and HTML, univariate forecasts, the daily cost math for production AI SQL pipelines.
6. Model Serving. Endpoints, the three fields that decide capacity and cost, the chat-completion payload, the five moving pieces of a production recommender.
7. Foundation Models. Five major providers, the External Models config, the vendor-swap pattern (Claude to Gemini in hours, not weeks), the three habits that keep swap cost low.
8. Vector Search and RAG. Six delta-sync arguments, three chunking strategies compared, the RAG function your app imports, end-to-end answer evaluation with traces.
9. MLflow 3 and UC Model Registry. Versions, aliases, tags (and what each is not for), five tracking calls and what each one writes, the experiment-to-production lifecycle.
10. Feature Store. Why SDP is the right producer, the six-file project layout, four parity-failure classes between offline and online stores and what causes each.
11. MLOps as a Practice. Seven sources every incident reads from, three deploy patterns (canary, shadow, blue-green), three retrain strategies, five golden signals for an ML endpoint.
12. Lakehouse Monitoring: Drift Detection. Six monitor parameters, the loop from drift alert to retraining, what to do when the baseline table is missing.
13. Distributed Deep Learning. Three signals that force distributed training, picking the flavor (data, model, hybrid) from the bottleneck, four pieces of GPU memory worked out for a 7B model.
14. Agent Bricks. Declarative classification and information-extraction agents, eval-set ingredients, the pre-compute pattern that makes small seed sets work.
15. Multi-Agent Supervisor and MCP. The supervisor build, synthetic-turn evaluation, three real conversations end to end, the auth-passthrough chain across child agents.
16. Lakebase: Operational Postgres for AI. Five alternatives compared, sub-10ms reads for AI apps, the lineage from Delta source through SDP into Postgres and onward to the endpoint.
17. Capstone: Retail Intelligence App. Ten stages, each anchored to an earlier chapter. The smoke test that confirms every stage of the platform is reachable, the new-data path through the recommender.
18. Certification and What's Next. The certification paths that actually map to the book, and the reading list the on-call team uses when something breaks.
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About
About the Book
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.
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Author
About the Author
Ritesh Modi
Ritesh Modi is Head of AI at MarketOnce and a former Forward Deployed Engineer at Microsoft. He has spent more than a decade building and shipping production systems across cloud, distributed computing, and applied machine learning, working with organizations ranging from global enterprises to fast-moving startups. His recent work focuses on applied large language models, designing systems that turn pretrained models into reliable, task-specific tools.
Ritesh has authored multiple technology books and speaks regularly at industry conferences on AI, cloud architecture, and software engineering. His writing philosophy rests on a simple belief: the best technical books are written by practitioners who still remember what it felt like to not understand something, not by experts who have forgotten. Every explanation in this book was tested against that standard, if it would not have made sense to him when he was first learning this material, it was rewritten until it did.
He writes, shares ideas, and connects with readers at www.riteshmodi.com. When he is not writing or building AI systems, he can be found mentoring engineers, exploring new architectures, or debugging a training run that should have converged three hours ago.
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