RAG Systems and Production Operations
By External Provider
About this course
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.
What you will learn
- Build and evaluate advanced RAG systems with Self-RAG and Corrective RAG patterns
- Implement secure, scalable vector database deployments with TLS and authentication
- Design production-ready APIs with monitoring, rate limiting, and performance optimization
- Execute cross-platform vector database migrations with data integrity checks