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

Setting and using environment variables

Before deep diving into the different deployment techniques, we need to structure our application to enable reliable, fast, and secure deployments. One of the key things in this process is handling configuration variables: a database URL, an external API token, a debug flag, and so on. When handling those variables, it's necessary to handle them dynamically instead of hardcoding them in your source code. Why?

First of all, those variables will likely be different in your local environment and in production. Typically, your database URL will point to a local database on your computer while developing but will point to a proper production database in production. This is even more true if you want to have other environments such as a staging or pre-production environment. Furthermore, if we need to change one of the values, we'll have to change the code, commit it, and deploy it again. Thus, we need a convenient mechanism to set those...