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Phase 8: LLMOps Infrastructure

📖 Deep-Dive Index

1. Vector Databases

  • Concepts: Embeddings, High-dimensional space, HNSW indexing.
  • Milvus / Qdrant: Deploying stateful, scalable vector databases on Kubernetes. Storage volumes, memory management.

2. AI Gateways & Middleware

  • The Problem: Hardcoding OpenAI API keys in 50 microservices leads to cost overruns and security breaches.
  • The Solution (AI Gateway): MLflow or LiteLLM. Centralizing API keys, tracking token usage per microservice, routing between OpenAI, Anthropic, and local LLMs.

🛠️ Job-Essential Exercises

  1. RAG Backend Deployment:
  2. Write a Helm chart or Kustomize overlay to deploy a single-node Milvus vector database onto your Kubernetes cluster.
  3. The AI Gateway Control:
  4. Deploy LiteLLM. Configure it with a master OpenAI key. Generate a sub-key specifically for your Frontend service, strictly rate-limiting it to $10/month. Route requests through the gateway and observe the dashboard tracking the token usage.