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FastAPI Tutorial

FastAPI is a modern Python web framework for building APIs — interfaces that let one program talk to another over HTTP. It's fast, type-driven, beginner-friendly, and auto-generates interactive documentation.

This tutorial takes you from "what is an API?" all the way to deploying a containerized ML model. Every chapter includes runnable code, clear explanations of what's happening under the hood, and practical pitfalls to avoid.

Topics

# Topic Read
1 Introduction — what FastAPI is, sync vs async 01-introduction
2 Installation & First App — pip, uvicorn, hello world 02-installation-and-first-app
3 Path & Query Parameters 03-path-and-query-params
4 Request Body & Pydantic Models 04-request-body-pydantic
5 Response Models & Status Codes 05-response-and-status-codes
6 Error Handling with HTTPException 06-error-handling
7 Full CRUD Example — Patient Records 07-crud-patients-example
8 Pydantic Deep Dive 08-pydantic-deep-dive
9 Async / Await — When & How 09-async-and-await
10 Dependency Injection (Depends) 10-dependencies
11 Middleware & CORS 11-middleware-cors
12 Authentication — OAuth2 + JWT 12-auth-jwt
13 Deploy an ML Model 13-ml-deployment
14 Docker & Cloud Deployment 14-docker-deployment

How this tutorial is organized

Every chapter follows the same beginner-friendly format:

  1. Short intro — what it is and why it exists.
  2. Runnable code examples — copy into main.py, run with uvicorn.
  3. "How it executes" — what's actually happening in the request lifecycle.
  4. Common pitfalls — what beginners typically get wrong.
  5. Next link — pointer to the next chapter.

Prerequisites

  • Basic Python — variables, functions, classes, dictionaries.
  • Comfortable installing packages with pip.
  • A code editor (VS Code recommended) and a terminal.

What you'll be able to do after this tutorial

  • Build a complete REST API from scratch.
  • Validate inputs and serialize outputs with Pydantic.
  • Handle path/query/body parameters cleanly.
  • Add authentication with OAuth2 + JWT.
  • Use dependency injection for testable code.
  • Deploy a trained ML model as an API.
  • Containerize with Docker and deploy to any cloud.

A note on the runnable code

FastAPI needs the uvicorn server. It can't run inside the browser like the Python tutorial's ▶ Run button. To try any example:

# 1. Install once
pip install fastapi uvicorn

# 2. Save the code as main.py
# 3. Run
uvicorn main:app --reload

# 4. Open http://127.0.0.1:8000 in your browser

Ready? → Start with Introduction