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Module 13: Deploying RAG Systems Slides

Slide walkthrough for Module 13 of Production-Grade RAG Systems Engineering: Dockerizing AI systems, Kubernetes for AI, GPU infrastructure, and CI/CD for...

This slide page is the visual review companion for the full course module. Use it to recap the architecture, examples, exercises, production warnings, and takeaways after reading the lesson.

Slide Outline

  1. Deploying RAG Systems - Dockerizing AI systems, Kubernetes for AI, GPU infrastructure, and CI/CD for AI applications
  2. Learning Objectives - 4 outcomes for this module
  3. Why This Module Matters - Deploying RAG to production is where most projects stall. The gap between localhost demo and Kubernetes production is en
  4. Dockerizing RAG - Lesson section from the full module
  5. Kubernetes for AI - Lesson section from the full module
  6. CI/CD for AI - Lesson section from the full module
  7. Hands-On Labs - 2 hands-on labs
  8. Key Takeaways - 5 points to remember

Learning Objectives

  • Containerize RAG systems with Docker
  • Deploy on Kubernetes with proper resource management
  • Configure GPU inference for embedding models
  • Build CI/CD pipelines for AI applications

Why This Module Matters

Deploying RAG to production is where most projects stall. The gap between localhost demo and Kubernetes production is enormous. This module gives you the deployment patterns that bridge that gap.

Key Takeaways

  • RAG has mixed workloads: CPU (API), GPU (embeddings), stateful (vector DB)
  • Scale RAG API with HPA on request rate, embedding service on queue depth
  • Qdrant needs persistent storage — use StatefulSet, not Deployment
  • AI CI/CD must include quality gates — not just tests, but evaluation metrics
  • Multi-stage Docker builds keep production images small and secure

Hands-On Labs

  1. Deploy RAG on Kubernetes

    Deploy the full RAG stack on a Kind cluster.

    40 min - Advanced

    • Build Docker images for RAG API and embedding service
    • Deploy Qdrant StatefulSet and Redis on Kubernetes
    • Deploy RAG API with HPA
    • Test end-to-end with port-forward

    View lab files on GitHub

  2. CI/CD with Quality Gates

    Build a pipeline that blocks deploys on quality regression.

    30 min - Advanced

    • Add retrieval quality tests to CI
    • Run hallucination detection on a test set
    • Set quality thresholds (block if precision < 0.8)
    • Deploy only if all quality gates pass

    View lab files on GitHub

Read the full module | Back to course curriculum