~/talk

Brag Your RAG with the MLOPS Swag

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Live demo builds three RAG paths: LangChain + Pinecone, Canopy + Pinecone, and a pure LangChain variant without a vector DB; all deploy on Kubernetes and the Canopy+Pinecone flow successfully answers a query about the 2024 Olympics and returns its source, proving grounding fixes hallucinations. Embedding pipeline runs with Ray on GKE, stores vectors in PostgreSQL/PGVector on Cloud SQL, streams data with GCS Fuse to avoid idle GPUs, and targets multi‑GPU A3 nodes plus secondary boot disks and service extensions for efficient serving.

AI-written summary · about

  1. 0:00Introduction and agenda
  2. 1:14LLM customization spectrum
  3. 3:49Prompt engineering and RAG
  4. 6:58Open source RAG implementation
  5. 8:36RAG demo and comparison
  6. 12:18Technical code walkthrough
  7. 13:23MLOps pipelines on GKE
  8. 16:00Deep dive into embeddings
  9. 17:11Optimizing storage with FUSE
  10. 18:07Advanced training patterns
  11. 24:14Q&A and session closing
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