Part three of the Ollama production series: keep Ollama private, add API key authentication and TLS, cap what clients can send, and stop leaking errors. Tested FastAPI code included.

Notes from benchmarking ML.NET against scikit-learn, XGBoost and LightGBM on open clinical datasets: three setup problems that will save you some debugging.

This follow-up post builds on the previous article about creating a FastAPI wrapper for Ollama models. It explores what’s needed to move from a dev-friendly API to a more production-grade service, including API authentication, rate limiting, request validation, load balancing, and monitoring. FastAPI performance a concern? Read on to find out more!

This series of follow-up posts builds on the original article about creating a FastAPI wrapper for Ollama models. These posts explore what’s needed to move from a dev-friendly API to a more production-grade service, including API authentication, rate limiting, request validation, load balancing, and monitoring.

"How AI Works: From Sorcery to Science" by Ronald T. Kneusel stands out as a refreshingly clear and engaging primer on artificial intelligence, aimed at breaking down barriers in a field that often seems shrouded in complexity and mystique.