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Building Data Science Applications with FastAPI

Building Data Science Applications with FastAPI

By : Voron
4.7 (16)
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Building Data Science Applications with FastAPI

Building Data Science Applications with FastAPI

4.7 (16)
By: 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)
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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

Throughout this book, we'll assume you have access to a Unix-based environment, such as a Linux distribution or macOS.

If they haven't done so already, macOS users should install the Homebrew package (https://brew.sh), which helps a lot in installing command-line tools.

If you are a Windows user, you should enable Windows Subsystem for Linux (https://docs.microsoft.com/windows/wsl/install-win10), WSL, and install a Linux distribution (such as Ubuntu) that will run alongside the Windows environment, which should give you access to all the required tools. There are currently two versions of WSL, WSL and WSL2. Depending on your Windows version, you might not be able to install the newest version. However, we do recommend using WSL2 if your Windows installation supports it.

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