RAG Systems and Production Operations

Overview

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This advanced course transforms you into an enterprise-level ML engineer capable of designing, implementing, and operating sophisticated retrieval-augmented generation (RAG) systems. You'll progress from foundational RAG architecture to cutting-edge patterns like Self-RAG and Corrective RAG, then dive deep into production operations including secure deployment, performance optimization, and cross-platform migration. By combining hands-on projects with real-world enterprise requirements, you'll learn to build AI systems that deliver accurate, grounded responses at scale. Each module builds practical skills used by senior ML engineers in high-stakes domains like legal tech, healthcare, and finance. Who this is for: Experienced software engineers and data scientists ready to build production-grade AI applications. Strong Python programming and basic machine learning knowledge required.

Syllabus

Understand RAG Basics

This foundational module demystifies Retrieval-Augmented Generation. You will learn why RAG is essential for creating reliable AI systems and explore the role and function of each component in its architecture. You will finish by sketching a RAG data flow diagram to solidify your theoretical understanding.

Advanced RAG Patterns

Go beyond basic RAG to build robust, self-correcting AI systems. This 2-hour course teaches intermediate developers to implement Corrective, Self, and Agentic RAG patterns. Through hands-on A/B testing and performance analysis, you’ll learn to architect, evaluate, and defend trustworthy, production-ready pipelines that solve complex, multi-hop queries with precision.

Deploy Vector DBs Securely

Move AI from local to production with this hands-on course. Master essential "last-mile" skills: containerize databases with Docker, implement TLS and RBAC security, and monitor health via Grafana. Learn to analyze performance for autoscaling, ensuring your enterprise-grade vector database deployments are secure, scalable, and production-ready.

Optimize and Migrate Vectors

Optimize and Migrate Vectors is a 90‑minute, hands‑on intermediate course for ML engineers to master vector‑database operations. Learn performance tuning to cut latency up to 40 % and script zero‑loss migrations of 100k+ vectors from Chroma to Weaviate using Python and Docker.

Production RAG Pipeline

In this project, you'll build a production-grade RAG system that synthesizes everything learned throughout the program: vector database deployment, advanced RAG patterns, security, monitoring, and performance optimization. This comprehensive project simulates enterprise requirements and produces strong portfolio evidence of end-to-end ML engineering capability.