How to Serve a PyTorch Model with FastAPI: A Complete Guide Most machine learning tutorials stop at model training. You get a trained model, a good validation score, and then — nothing. No one tells you how to actually use that model in a real application. In this tutorial I'll show you exactly how to take a trained PyTorch model and serve it as a REST API using FastAPI. By the end you'll have a working inference endpoint that any frontend or application can call to get predictions. I built this exact pipeline for my house price estimator project — a PyTorch MLP model served via FastAPI with a React frontend. Everything in this tutorial comes from real production experience. Prerequisites Python 3.8+ Basic PyTorch knowledge Basic understanding of REST APIs pip installed What We're Building Trained PyTorch Model (.pth file) ↓ FastAPI Server ↓ POST /predict endpoint ↓ Returns prediction as JSON A client sends input features as JSON.…