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.

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.

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 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.

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.