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

Technical requirements

For this chapter, you'll need a Python virtual environment, similar to the one we set up in Chapter 1, Python Development Environment Setup.

For the Testing with a database section, you'll need a running MongoDB server on your local computer. The easiest way to do this is to run it as a Docker container. If you've never used Docker before, we recommend that you read the Get Started tutorial in the official documentation: https://docs.docker.com/get-started/. Once done, you'll be able to run a MongoDB server with this simple command:

$ docker run -d --name fastapi-mongo -p 27017:27017 mongo:4.4

The MongoDB server instance will then be available on your local computer on port 27017.

You can find all the code examples for this chapter in its dedicated GitHub repository: https://github.com/PacktPublishing/Building-Data-Science-Applications-with-FastAPI/tree/main/chapter9.