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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

Getting started with OpenCV

Computer vision is a field related to machine learning that aims at developing algorithms and systems to analyze images and videos automatically. A typical example of computer vision's application is face detection: a system automatically detecting human faces in an image. This is the kind of system we'll build in this chapter.

To help us in this task, we'll use OpenCV, which is one of the most popular computer vision libraries. It's written in C and C++ but has bindings to make it usable in many other programming languages, including Python. We could have used scikit-learn to develop a face detection model, but we'll see that OpenCV already includes all the necessary tools to perform this task without having to manually train and tune machine learning estimators.

To begin with OpenCV, we'll implement a simple Python script to perform face detection locally using a computer webcam:

  1. The first step is, of course...
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