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

Congratulations! You've learned another important aspect of FastAPI: designing and managing data models with Pydantic. You should now be confident about creating models and applying validation at a field level, with built-in options and types, and also by implementing your own validation methods. You also know how to apply validation at an object level to check consistency between several fields. You also reviewed a common pattern, leveraging model inheritance to avoid code duplication and repetition while defining your model variations. Finally, you learned how to correctly work with Pydantic model instances in order to transform and update them in an efficient and readable way.

You know almost all the features of FastAPI by now. There is a last very powerful one for you to learn: dependency injections. These will allow you to define your own logic and values to directly inject into your path operation functions, as you do for path parameters and payload objects, which...