Course Breakdown (4 Modules – 4 Hours Each)
Building Retrieval Agents on Databricks
This module focuses on RAG-based systems.
You’ll Learn:
- Parsing unstructured documents
- Chunking strategies for retrieval
- Embedding generation
- Vector search setup
- Agent lifecycle management
- Logging agents using MLflow
- Building with Agent Bricks
Why It’s Important
This is the core skillset for enterprise GenAI. Most real-world AI systems today use:
- RAG pipelines
- Vector databases
- Governance layers
For someone already working with large datasets (like your 25M+ row tables), this is highly relevant.
Building Single-Agent Applications on Databricks
Focuses on structured, tool-using agents.
Covers:
- Agent fundamentals
- Using Unity Catalog functions as tools
- Tracing & monitoring with MLflow
- Frameworks like LangChain
- Deployment with Agent Bricks
Why It Matters
You’ll learn:
- How to build production-grade agents
- Governance with Unity Catalog
- Reproducibility (very important in enterprise AI)
This is highly valuable for roles like:
- GenAI Engineer
- LLM Engineer
- AI Platform Engineer
Generative AI Application Evaluation and Governance
This module is critical for enterprise adoption.
Topics:
- Evaluation frameworks
- Security & governance
- Performance & cost analysis
- End-to-end system evaluation
Enterprise Value
Most engineers can build agents.
Very few know how to:
- Evaluate hallucination risk
- Measure retrieval quality
- Govern production AI systems
This module differentiates you at a senior level.
Generative AI Application Deployment and Monitoring
This is LLMOps.
Covers:
- Model serving
- Batch & real-time deployment
- Monitoring with Lakehouse Monitoring
- Operational best practices
This aligns with your DevOps + Databricks experience.
Skill Level Analysis (For You Specifically)
Based on your background:
- Strong SQL
- Databricks performance optimization
- Large-scale table engineering
- Tech Lead responsibilities
You already meet:
- Advanced SQL ✔
- Databricks workspace familiarity ✔
- MLflow basic understanding ✔
- Governance & catalog concepts ✔
You may need to strengthen:
- Advanced RAG architectures
- Agent reasoning patterns
- Evaluation metrics for GenAI
Career Impact
If completed properly, this course helps you transition into:
- Senior Data Engineer (GenAI Focus)
- AI Platform Engineer
- LLM Engineer
- Applied GenAI Engineer
For UK market (like HomeServe-type companies), this is highly valuable.
Is It Worth It?
Yes, if:
- You want to pivot into GenAI engineering
- You want higher salary band (GenAI roles pay premium)
- You want to future-proof your career
Maybe Not If:
- You only want pure SQL/Data Warehousing roles
- You don’t plan to build AI applications
Strategic Recommendation for You
Given your profile:
- Take this course.
- Build one production-style RAG demo.
- Add:
- MLflow tracking
- Evaluation metrics
- Deployment pipeline
- Add to resume as: “Designed and deployed enterprise-grade Retrieval-Augmented Generation system using Databricks, MLflow, and Unity Catalog governance.”
That will significantly upgrade your resume.
4-Week Structured Learning Roadmap
Goal: Become Production-Ready GenAI Engineer on Databricks
WEEK 1 — RAG Foundations + Vector Search
Objective:
Understand and build a complete Retrieval-Augmented Generation (RAG) pipeline.
Concepts to Master
- RAG architecture (end-to-end)
- Embeddings
- Vector similarity search
- Chunking strategies
- Hallucination causes
Tools to Focus On
- Databricks
- MLflow
- LangChain
- Databricks Vector Search
- Unity Catalog basics
Hands-On Project (Mini Project 1)
Build: Internal Document Q&A Bot
Steps:
- Take 10–20 PDFs (policies, documentation, insurance docs, etc.)
- Parse documents
- Chunk content (try multiple chunk sizes)
- Generate embeddings
- Store in vector index
- Build retrieval chain
- Add evaluation logging using MLflow
Engineering Focus (Important for You)
Since you’re a data engineer:
- Compare chunk sizes (200 vs 500 vs 1000 tokens)
- Measure retrieval latency
- Log cost + token usage
- Store embeddings in Delta
Treat it like a production pipeline, not a demo.
WEEK 2 — Agent Engineering + Tool Usage
Objective:
Move from RAG to intelligent agents.
Concepts
- What is an AI agent?
- Tool calling
- Multi-step reasoning
- ReAct pattern
- Agent vs chain difference
Tools
- LangChain Agents
- MLflow tracing
- Agent Bricks
- Unity Catalog Functions
Hands-On Project (Mini Project 2)
Build: Data Assistant Agent
Agent should:
- Query a Delta table
- Call SQL function via Unity Catalog
- Retrieve documents (RAG)
- Answer business questions
Example:
This uses:
- SQL tool
- Retrieval tool
- LLM reasoning
Advanced Focus
- Add tracing in MLflow
- Log intermediate reasoning steps
- Compare single-agent vs RAG-only
WEEK 3 — Evaluation, Governance & Security
Objective:
Become enterprise-grade engineer (this differentiates seniors)
Concepts
- Hallucination evaluation
- Retrieval precision/recall
- Cost tracking
- Guardrails
- Prompt injection risks
- PII handling
Tools
- MLflow evaluation
- Unity Catalog governance
- Lakehouse Monitoring
Hands-On Project (Mini Project 3)
Add evaluation layer to Week 1 + 2 systems:
- Create test dataset (question-answer pairs)
- Measure:
- Faithfulness
- Retrieval accuracy
- Response relevance
- Track:
- Token usage
- Latency
- Cost per query
Engineering Mindset
Create:
- Evaluation notebook
- Governance checklist
- Production architecture diagram
This is what hiring managers look for.
WEEK 4 — Deployment + LLMOps
Objective:
Deploy like a production system.
Concepts
- Model serving
- Batch vs real-time inference
- Monitoring drift
- Logging strategies
- CI/CD for GenAI
Tools
- Databricks Model Serving
- MLflow model registry
- Lakehouse Monitoring
Final Capstone Project
Enterprise Customer Intelligence Assistant
Build:
Architecture:
User → API → Agent
↓
Vector Search + SQL Tool
↓
MLflow Logging
↓
Model Serving Endpoint
Must Include:
- RAG pipeline
- Tool-based agent
- MLflow tracking
- Evaluation metrics
- Deployed endpoint
- Monitoring
Weekly Time Allocation (Working Professional Plan)
| Activity | Hours |
|---|---|
| Theory | 2 |
| Coding | 4 |
| Optimization & evaluation | 2 |
| Documentation | 1–2 |
After 4 Weeks — You Should Be Able To:
✅ Build RAG systems
✅ Build tool-using agents
✅ Evaluate hallucinations
✅ Deploy using Model Serving
✅ Implement monitoring
✅ Design GenAI architecture
Resume Upgrade Line (After Completion)
Designed and deployed enterprise-grade Retrieval-Augmented Generation (RAG) and multi-tool AI agents on Databricks using MLflow, Vector Search, Unity Catalog governance, and Model Serving with evaluation and monitoring framework.
If You Want To Go One Level Higher (Optional Week 5–6)
- Multi-agent systems
- Memory management
- Fine-tuning small models
- Cost optimization at scale
- Prompt versioning strategy