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)

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FastAPI Server

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POST /predict endpoint

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Returns prediction as JSON

A client sends input features as JSON.…