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.
This AI and MLOps blog is for engineers moving from experiments to dependable software. I write about the design choices, failures, and trade-offs behind production ML pipelines, agentic AI systems, retrieval, model serving, and observability.
Each article starts with something I built or debugged. Expect implementation detail, honest constraints, and explanations of why a tool earned its place—not generic tool lists or rewritten documentation. Start with the roadmap if you are learning, or the MLOps pipeline walkthrough if you already ship Python services.
The collection currently covers production MLOps architecture, a practical AI/ML learning roadmap, and choosing a maintainable ML platform stack. Examples include concrete configurations and the mistakes behind them. Future posts will continue that thread with retrieval evaluation, agent reliability, and operating FastAPI services under real load.
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.