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

In this chapter, you've learned how to work with one of the latest web technologies available: WebSocket. You are now able to open a two-way communication channel between a client and a server, allowing you to implement applications with real-time constraints. As you've seen, FastAPI makes it very easy to add such endpoints. Still, the way of thinking inside a WebSocket logic is quite different from traditional HTTP endpoints: managing an infinite loop and handling several tasks at a time are completely new challenges. Fortunately, the asynchronous nature of the framework makes our life easier in this matter and helps us write concurrent code that is easily understandable.

Finally, we also had a quick overview of the challenges to solve when handling multiple clients that share messages between them. You saw that message broker software such as Apache Kafka or RabbitMQ is necessary to make this use case reliable across several server processes.

You are now acquainted...