Training a model is the easy part. Serving it under real traffic, with low latency, predictable cost, and a way to roll back when it goes wrong, is where most ML projects quietly die. Here's how I take a model from a Jupyter notebook to a production API that doesn't page me at 3am.
Engineering Craft
TypeScript, CI/CD, databases, observability -- the skills that make code production-ready.
