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.

This blog post explains how to use FastAPI to create an API for Ollama models, enabling wider application access and enhanced security. It provides code examples for using ollama.generate() and ollama.chat() and discusses best practices for deployment.