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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

Summary

In this chapter, we showed how WebSockets can help us bring a more interactive experience to users. Thanks to OpenCV, we were able to quickly implement a face detection system. Then, we integrated it into a WebSocket endpoint with the help of FastAPI. Finally, by using a modern JavaScript API, we sent video input and displayed algorithm results directly in the browser. All in all, a project like this might sound complex to make at first, but we saw that powerful tools such as FastAPI enable us to get results in a very short time and with very comprehensible source code.

This is the end of the book and our FastAPI journey together. We sincerely hope that you liked it and that you learned a lot along the way. We covered many subjects, sometimes just by scratching the surface, but you should now be ready to build your own projects with FastAPI and serve up smart data science algorithms. Be sure to check out all the external resources we mentioned along the way, as they will...

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