I write about stuff I've actually built and problems I've actually run into. If I haven't done it myself, I don't write about it.
Every ML tutorial ends at model.fit(). I wanted to see what happens when you actually try to deploy the thing. Turns out, that's where all the real engineering lives.
I counted 47 MLOps tools and tried way too many of them. Here's what I actually use after going through that process, and why each one earned its spot.
Every RAG tutorial works on the demo. Then you try it on your own documents and the answers are garbage. Here's what actually matters — chunking, retrieval tuning, and the gotchas nobody warns you about.
The stuff I wish someone had told me before I deployed my first FastAPI service. Middleware patterns, logging that's actually useful, Prometheus integration, and graceful shutdown.