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

Chapter 13: Creating an Efficient Prediction API Endpoint with FastAPI

In the previous chapters, we introduced the most common data science techniques and libraries largely used in the Python community. Thanks to those tools, we can now build machine learning models that can make efficient predictions and classify data. Of course, we now have to think about a convenient interface so that we can take advantage of their intelligence. This way, microservices or frontend applications can ask our model to make predictions to improve the user experience or business operations.

In this chapter, we'll learn how to do that with FastAPI. As we've seen throughout this book, FastAPI allows us to implement very efficient REST APIs with clear and lightweight syntax. In this chapter, you'll learn how to do this as efficiently as possible so that it can serve thousands of prediction requests. To help us with this task, we'll introduce another library, Joblib, that provides tools...