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

Caching results with Joblib

If your model takes time to make predictions, it may be interesting to cache the results: if the prediction for a particular input has already been done, it makes sense to return the same result we saved on disk, rather than running the computations again. In this section, we'll learn how to do this with the help of Joblib.

Joblib provides us with a very convenient and easy-to-use tool to do this, so the implementation is quite straightforward. The main concern will be about whether we should choose standard or async functions to implement the endpoints and dependencies. This will allow us to explain some of the technical details of FastAPI in more detail.

We'll build upon the example we provided in the previous section. The first thing we must do is initialize a Joblib Memory class, which is the helper for caching functions results. Then, we can add a decorator to the functions we want to cache. You can see this in the following example...