Deploy Machine Learning APIs on AWS with FastAPI and Docker
Deploy Machine Learning APIs on AWS with FastAPI and Docker You’ve trained your ML model. Now what? Getting it off your laptop and into production is where most tutorials leave you hanging. This guide walks you through the full machine learning API deployment process — from wrapping your model in FastAPI to containerizing it with Docker and shipping it to AWS. This is written for data scientists and backend developers who know Python and have a basic grasp of REST APIs but haven’t tackled cloud deployment yet. If you’ve been Googling “deploy ML model AWS” and keep landing on outdated walkthroughs, you’re in the right place. Here’s what you’ll get out of this: How to build a production-ready FastAPI application around your ML model — not a toy demo, but something that handles real traffic cleanly How to containerize your machine learning API with Docker so it runs the same way everywhere, no “works on my machine” surpr...