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

Managing Python dependencies

Throughout this book, we've installed libraries using pip to add some useful features to our application: FastAPI, of course, but also SQLAlchemy, Tortoise ORM, Pytest, and so on. When deploying a project to a new environment, such as a production server, we have to make sure all those dependencies are installed for our application to work properly. This is also true if you have colleagues that also need to work on the project: they need to know the dependencies they must install on their machines.

Fortunately, pip comes with a solution for this so that we don't have to remember all this in our heads. Indeed, most Python projects define a requirements.txt file, which contains a list of all Python dependencies. It usually lives at the root of your project. pip has a special option for reading this file and installing all the needed dependencies.

When you already have a working environment, such as the one we've used since the beginning...