Databricks RAG Master Course: Build Production AI Pipelines

About this course

This course contains the use of artificial intelligence.

Stop stitching together disconnected AI tools. Build RAG the way it's meant to work — inside Databricks.

If your company runs on Databricks and you've been asked to build an AI search or chatbot feature over your own data, this course takes you from raw documents in Unity Catalog to a fully deployed, governed RAG pipeline — step by step, with real, runnable notebooks.

This course is for you if:

• You already work in Databricks (Spark, Delta Lake, notebooks) and need to add an AI/RAG feature
• You've tried generic RAG tutorials that ignore governance and don't fit your company's platform
• You want to understand Mosaic AI Vector Search and Agent Bricks without guesswork
• You need a production-ready pipeline, not just a notebook demo
• You want a real, deployable project for your portfolio or internal team

What you will learn:

• Understand the full RAG architecture and how it maps to the Databricks AI stack
• Choose the right chunking strategy for different document types
• Generate embeddings and build a Mosaic AI Vector Search index from Delta tables
• Build a complete retrieval-to-response RAG query pipeline
• Use Agent Bricks to configure a managed, multi-step retrieval agent
• Build a golden evaluation set and score retrieval and answer quality
• Deploy a RAG pipeline as a governed Model Serving endpoint
• Set Unity Catalog permissions to control who can access your AI agent
• Keep your vector index automatically in sync with changing source data
• Monitor cost, latency, and usage of a deployed RAG agent in production

Requirements:

• A Databricks workspace with Unity Catalog and Mosaic AI features enabled

Basic Python

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