Building Data Science Applications with FastAPI

Get well-versed with FastAPI features and best practices for testing, monitoring, and deployment to run high-quality and robust data science applications

Key Features

• Cover the concepts of the FastAPI framework, including aspects relating to asynchronous programming, type hinting, and dependency injection
• Develop efficient RESTful APIs for data science with modern Python
• Build, test, and deploy high performing data science and machine learning systems with FastAPI

Book Description

FastAPI is a web framework for building APIs with Python 3.6 and its later versions based on standard Python-type hints. With this book, you’ll be able to create fast and reliable data science API backends using practical examples.

This book starts with the basics of the FastAPI framework and associated modern Python programming language concepts. You'll be taken through all the aspects of the framework, including its powerful dependency injection system and how you can use it to communicate with databases, implement authentication and integrate machine learning models. Later, you’ll cover best practices relating to testing and deployment to run a high-quality and robust application. You’ll also be introduced to the extensive ecosystem of Python data science packages. As you progress, you’ll learn how to build data science applications in Python using FastAPI. The book also demonstrates how to develop fast and efficient machine learning prediction backends and test them to achieve the best performance. Finally, you’ll see how to implement a real-time face detection system using WebSockets and a web browser as a client.

By the end of this FastAPI book, you’ll have not only learned how to implement Python in data science projects but also how to maintain and design them to meet high programming standards with the help of FastAPI.

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
• Implement a FastAPI dependency to efficiently run a machine learning model
• Integrate a simple face detection algorithm in a FastAPI backend
• Integrate common Python data science libraries in a web backend
• Deploy a performant and reliable web backend for a data science application

Who this book is for

This Python data science 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 RESTful API with FastAPI
4. Managing pydantic Data Models in FastAPI
5. Dependency Injections 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 NumPy and Pandas
12. Training Machine Learning Models with scikit-learn
13. Creating an Efficient Prediction API Endpoint with FastAPI
14. Implement a Real-Time Face Detection System Using WebSockets with FastAPI and OpenCV

François Voron is 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 API.

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.