Book Image

Building Data Science Applications with FastAPI

By : François Voron
5 (1)
Book Image

Building Data Science Applications with FastAPI

5 (1)
By: François Voron

Overview of this book

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.
Table of Contents (19 chapters)
1
Section 1: Introduction to Python and FastAPI
7
Section 2: Build and Deploy a Complete Web Backend with FastAPI
13
Section 3: Build a Data Science API with Python and FastAPI

Summary

Well done! You should now be comfortable with one of the most iconic features of FastAPI: dependency injections. By implementing your own dependencies, you'll be able to keep common logic that you wish to reuse across your API separate from the endpoints' logic. This will make your project clean and maintainable while retaining maximum readability: dependencies just need to be declared as arguments of the path operation functions, which will help to understand the intent without having to read the body of the function.

Those dependencies can be both simple wrappers to retrieve and validate request parameters, or complex services performing machine learning tasks. Thanks to the class-based approach, you can indeed set dynamic parameters or keep a local state for your most advanced tasks.

Finally, those dependencies can also be used at a router or global level, allowing you to perform common logic or checks for a set of routes or a whole application.

That&apos...