Get well-versed with FastAPI features and best practices for testing, monitoring, and deployment to run high-quality and robust data science applications

Key Features

Cover the concepts of the FastAPI framework, including aspects relating to asynchronous programming, type hinting, and dependency injection

Develop efficient RESTful APIs for data science with modern Python

Build, test, and deploy high perfog data science and machine learning systems with FastAPI

Book Description

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- 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.

What you will learn

Explore the basics of modern Python and async I/O programming

Get to grips with basic and advanced concepts of the FastAPI framework

Implement a FastAPI dependency to efficiently run a machine learning model

Integrate a simple face detection algorithm in a FastAPI backend

Integrate common Python data science libraries in a web backend

Deploy a performant and reliable web backend for a data science application

Who this book is for

This Python data science book is for data scientists and software developers interested in gaining knowledge of FastAPI and its ecosystem to build data science applications. Basic knowledge of data science and machine learning concepts and how to apply them in Python is recommended.

Table of Contents

Python Development Environment Setup

Python Programming Specificities

Developing RESTful API with FastAPI

Managing pydantic Data Models in FastAPI

Dependency Injections in FastAPI

Databases and Asynchronous ORMs

Managing Authentication and Security in FastAPI

Defining WebSockets for Two-Way Interactive Communication in FastAPI

Testing an API Asynchronously with pytest and HTTPX

Deploying a FastAPI Project

Introduction to NumPy and Pandas

Training Machine Learning Models with scikit-learn

Creating an Efficient Prediction API Endpoint with FastAPI

Implement a Real- Face Detection System Using WebSockets with FastAPI and OpenCV



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