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Building Data Science Applications with FastAPI

Building Data Science Applications with FastAPI

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

Building Data Science Applications with FastAPI

4.7 (16)
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)
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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

Chapter 9: Testing an API Asynchronously with pytest and HTTPX

In software development, a significant part of the developer's work should be dedicated to writing tests. At first, you may be tempted to manually test your application by running it, making a few requests, and arbitrarily deciding that "everything works". However, this approach is flawed and can't guarantee that your program works in every circumstance and that you didn't break things along the way.

That's why several disciplines have emerged regarding software testing: unit tests, integration tests, E2E tests, acceptance tests, and more. These techniques aim to validate the functionality of the software from a micro level, where we test single functions (unit tests), to a macro level, where we test a global feature that delivers value to the user (acceptance tests). In this chapter, we'll focus on the first level: unit testing.

Unit tests are short programs designed to verify that...

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Building Data Science Applications with FastAPI
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