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Video by MLflow via YouTube
In the ninth tutorial of the Mastering MLflow for GenAI series, Jules Damji (Databricks) builds a complete RAG application, instrumented with full MLflow observability—from query and document embedding and semantic search retrieval through LLM generation, performance analysis, and RAGAS quality evaluation.
What You’ll Learn:
🔹 End-to-end RAG pipeline instrumented as typed spans (PARSER, EMBEDDING, RETRIEVER, LLM, CHAIN): validate → embed → retrieve → assemble → generate → validate.
🔹 @mlflow.trace instrumentation plus mlflow.openai.autolog() for automatic LLM tracing.
🔹 Performance analysis across test queries: latency, token usage, cache hits, and estimated cost.
🔹 RAGAS Faithfulness and Context Relevance via mlflow.genai.evaluate() on traces with RETRIEVER spans.
🔹 Production notes: in-memory store and cosine similarity for teaching; swap in vector DBs and hybrid BM25 and semantic searches for real deployments.
🔹 MLflow UI & multi-level tracking and tracing: experiment config, per-query runs, per-step latency/tokens/cost, full pipeline timeline, span attributes, and latency bottlenecks.
Next in the Series: Notebook 1.10 covers the Multi-Agent Supervisor pattern with LangGraph.
Resources:
🔗 Notebook 1.9: https://github.com/dmatrix/mlflow-genai-tutorials/blob/main/09_complete_rag_application.ipynb
🎥 Full Series Playlist: https://youtube.com/playlist?list=PLaoPu6xpLk9EI99TuOjSgy-UuDWowJ_mR