Notes on machine learning
Technical notes written while learning: honest evaluation, data leakage, deployment, LLM applications and the mistakes behind each lesson. They come from the projects on this site, with the code one click away.
Publishing ten CLIs on PyPI: what nobody tells you
Names already taken, a new-project limit that is not in the documentation, and why the package name does not have to match the command name. Notes from publishing a full suite of tools.
Read article
From notebook to an inference API that survives production
A model trained in a notebook is not a product. Turning it into a service that validates its input, responds with predictable latency and can be monitored is the work that separates a demo from a system.
Read articleMLOps: the difference between a model in a notebook and one in production
A notebook is an exploration environment, not a system. What it takes for a model to work in production, maintain itself and not degrade silently.
Read article
Drift: how to detect that your model is degrading
The model does not change. The data does. And if nobody is watching, the model can be giving bad predictions for weeks without anyone knowing. How to monitor drift in practice.
Read articleShall we connect?
Let's talk: feedback, collaboration or an opportunity.
You can write to me about a project, a technical question, to give me feedback, or about an internship or a first junior role. I always reply.
Write to me