Mimir, Keeper of the Well of Wisdom
Databricks RAG Master Course: Build Production AI Pipelines
https://www.udemy.com/course/databricks-rag-master-course-build-
production-ai-pipelines/
Year : 2026
Language : English
Level : All Levels
Category : IT & Software
Subcategory : Other IT & Software
Duration : 1h 44m
Lectures : 6
Rating : 3.6/5 (4 reviews)
Students : 168
INSTRUCTOR(S)
HEADLINE
Master Databricks RAG. Build production AI pipelines, use
Mosaic AI Vector Search, and deploy secure enterprise
agents.
WHAT YOU'LL LEARN
* 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
* 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.
WHO IS THIS COURSE FOR
* 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.
DESCRIPTION
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 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.
COURSE CONTENT
Chapter 1: Getting Started with RAG on Databricks ? The
Enterprise AI Pattern
1. Getting Started with RAG on Databricks
Chapter 2: Building the Data Pipeline ? Ingest, Chunk, and
Embed
2. Building a RAG Pipeline with Databricks
Chapter 3: Working with Embeddings and Mosaic AI Vector Search
in Databricks
3. Working with Embeddings and Mosaic AI Vector Search in
Databricks
Chapter 4: Building RAG Pipeline with Databricks ? The
Retrieval Chain
4. Using Agent Bricks for RAG Workflows in Databricks
Chapter 5: Building and Optimizing Production RAG Agents on
Databricks
5. Building and Optimizing Production RAG Agents on
Databricks
Chapter 6: REAL-WORLD ASSETS (Downloadable)
6. REAL-WORLD ASSETS (Downloadable)
DATES
Published : 2026-08-15
Last Updated : 2026-08-14
If you fear the truth, dont come to my well.