Best Practices for ML Inference with FastAPI
July 26, 2026 | Reading Time: 8 min
As machine learning practitioners we need to serve ML models and in 2026 FastAPI is the way to do this. In this post I’m discussing some best practices for deploying ML inference with FastAPI. In case you’re not familiar, FastAPI is a python framework that enables creating HTTP API’s. The framework enables you to declare methods for each path you are serving. It’s built on top of other packages such as pydantic, starlette and OpenAPI which provide useful features.
ML Inference with BentoML
September 29, 2025 | Reading Time: 4 min
When it comes to deploying machine learning models into production, there’s no shortage of tools available. I’ve been exploring the landscape of ML inference frameworks, trying to understand the trade-offs and strengths of different options. I spent a bit of time investigating BentoML a while back, and really liked user-friendly design and focus on model serving. How Widely used is BentoML? For fun—and a bit of insight—I compared three well-known ML serving tools using Google Trends: BentoML, NVIDIA’s Triton Inference Server, and KServe.