Databricks RAG Master Course Build Production AI Pipelines

What you'll learn

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Configure Databricks Workspaces and Unity Catalog to build secure, governed Generative AI data pipelines and AI applications natively on your Lakehouse.
Ingest unstructured data, process text with Apache Spark, and deploy continuous-sync Mosaic AI Vector Search indexes without external databases.
Build highly accurate Retrieval-Augmented Generation (RAG) pipelines integrating LangChain with Databricks Foundation Model APIs for LLM hosting.
Evaluate RAG models for groundedness using MLflow and deploy production-ready REST API serving endpoints for autonomous Databricks Agent workflows.
Requirements

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You need intermediate proficiency in Python programming and a basic understanding of SQL and data warehousing concepts. You must have access to a Databricks environment (a free trial on AWS, Azure, or GCP works perfectly for this). While a fundamental understanding of what Large Language Models (LLMs) are is helpful, no prior experience with Databricks Mosaic AI or MLflow is required.
Description

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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

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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

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A Databricks workspace with Unity Catalog and Mosaic AI features enabled
Basic Python and SQL knowledge
Basic familiarity with Databricks notebooks (Spark experience helpful but not required)
Final project: You will build and deploy a complete, governed RAG pipeline - from document ingestion and chunking through vector indexing, evaluation, and a live Model Serving endpoint - that you can demo directly to your team, a client, or a future employer.
Who this course is for

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This course is specifically designed for Data Engineers, Machine Learning Engineers, and Backend Python Developers who want to build secure, enterprise-grade AI applications natively on top of their data. It is highly valuable for Cloud Architects and technical leads who are frustrated by the complexity and security risks of moving data to external vector databases, and who want to master the unified governance and deployment ecosystem provided by Databricks, Unity Catalog, and Mosaic AI.
Homepage

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