I'm a Machine Learning Engineer, and I spend my working days building and shipping ML systems that real teams depend on. I came into this straight out of a Computer Science degree, and production taught me the thing most tutorials skip: training a model is the easy part. Getting it deployed, monitored, reproducible, and actually trusted by the people using it — that's where the real work lives, and where most projects quietly die.

That gap is why I teach. My courses cover end-to-end MLOps using free and open-source tools, so you can build a complete pipeline without a corporate budget or a surprise cloud bill. Everything I show you is something I've run myself.

If you've trained models in notebooks and wondered how they become real services, I built this for you.