Learn all the features and best practices of FastAPI to build, deploy, and monitor powerful data science and AI apps, like object detection or image generation.

Key Features

• Uncover the secrets of FastAPI, including async I/O, type hinting, and dependency injection
• Learn to add authentication, authorization, and interaction with databases in a FastAPI backend
• Develop real-world projects using pre-trained AI models

Book Description

Building Data Science Applications with FastAPI is the go-to resource for creating efficient and dependable data science API backends. This second edition incorporates the latest Python and FastAPI advancements, along with two new AI projects – a real-time object detection system and a text-to-image generation platform using Stable Diffusion.

The book starts with the basics of FastAPI and modern Python programming. You'll grasp FastAPI's robust dependency injection system, which facilitates seamless database communication, authentication implementation, and ML model integration. As you progress, you'll learn testing and deployment best practices, guaranteeing high-quality, resilient applications.

Throughout the book, you'll build data science applications using FastAPI with the help of projects covering common AI use cases, such as object detection and text-to-image generation. These hands-on experiences will deepen your understanding of using FastAPI in real-world scenarios.

By the end of this book, you'll be well equipped to maintain, design, and monitor applications to meet the highest programming standards using FastAPI, empowering you to create fast and reliable data science API backends with ease while keeping up with the latest advancements.

What you will learn

• Explore the basics of modern Python and async I/O programming
• Get to grips with basic and advanced concepts of the FastAPI framework
• Deploy a performant and reliable web backend for a data science application
• Integrate common Python data science libraries into a web backend
• Integrate an object detection algorithm into a FastAPI backend
• Build a distributed text-to-image AI system with Stable Diffusion
• Add metrics and logging and learn how to monitor them

Who this book is for

This book is for data scientists and software developers interested in gaining knowledge of FastAPI and its ecosystem to build data science applications. Basic knowledge of data science and machine learning concepts and how to apply them in Python is recommended.

Table of Contents

1. Python Development Environment Setup
2. Python Programming Specificities
3. Developing a RESTful API with FastAPI
4. Managing Pydantic Data Models in FastAPI
5. Dependency Injection in FastAPI
6. Databases and Asynchronous ORMs
7. Managing Authentication and Security in FastAPI
8. Defining WebSockets for Two-Way Interactive Communication in FastAPI
9. Testing an API Asynchronously with pytest and HTTPX
10. Deploying a FastAPI Project
11. Introduction to Data Science in Python
12. Creating an Efficient Prediction API Endpoint with FastAPI
13. Implementing a Real-Time Object Detection System Using WebSockets with FastAPI
14. Creating a Distributed Text-to-Image AI System Using the Stable Diffusion Model
15. Monitoring the Health and Performance of a Data Science System

About the Author

François Voron graduated from the University of Saint-Étienne (France) and the University of Alicante (Spain) with a master's degree in machine learning and data mining. A full stack web developer and a data scientist, François has a proven track record working in the SaaS industry, with a special focus on Python backends and REST APIs. He is also the creator and maintainer of FastAPI Users, the #1 authentication library for FastAPI, and is one of the top experts in the FastAPI community.