Building Data Science Applications with FastAPI: Develop, manage, and deploy efficient machine learning applications with Python

Book Description

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 performing data science and machine learning systems with FastAPI

Book Description

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.

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

1. Python Development Environment Setup
2. Python Programming Specificities
3. Developing RESTful API with FastAPI
4. Managing pydantic Data Models in FastAPI
5. Dependency Injections in FastAPI
6. Databases and Asynchronous ORMs
7. Managing Authentication and Security in FastAPI
8. Defining WebSockets for Two-Way Interactive Communication in FastAPI
9. Testing an API Asynchronously with pytest and HTTPX
10. Deploying a FastAPI Project
11. Introduction to NumPy and Pandas
12. Training Machine Learning Models with scikit-learn
13. Creating an Efficient Prediction API Endpoint with FastAPI
14. Implement a Real-Time Face Detection System Using WebSockets with FastAPI and OpenCV

中文：

书名：使用FastAPI构建数据科学应用程序：使用Python开发、管理和部署高效的机器学习应用程序

熟悉FastAPI功能和测试、监控和部署的最佳做法，以运行高质量且强大的数据科学应用程序

主要特点

• 介绍FastAPI框架的概念，包括与异步编程、类型提示和依赖项注入相关的方面
• 使用现代Python开发高效的REST风格的数据科学API
• 使用FastAPI构建、测试和部署高性能数据科学和机器学习系统

图书描述

FastAPI是一个Web框架，用于使用Python3.6及其更高版本基于标准的Python型提示构建API。有了这本书，您将能够使用实际示例创建快速可靠的数据科学API后端。

本书从FastAPI框架的基础知识和相关的现代Python编程语言概念开始。您将了解该框架的所有方面，包括其强大的依赖注入系统，以及如何使用它与数据库通信、实现身份验证和集成机器学习模型。稍后，您将介绍与测试和部署有关的最佳实践，以运行高质量和健壮的应用程序。您还将了解到广泛的Python数据科学包生态系统。随着学习的深入，您将学习如何使用FastAPI在Python中构建数据科学应用程序。该书还演示了如何开发快速高效的机器学习预测后端，并对其进行测试以实现最佳性能。最后，您将了解如何使用WebSockets和Web浏览器作为客户端来实现实时人脸检测系统。

到这本FastAPI书结束时，您不仅已经学习了如何在数据科学项目中实现Python，而且还了解了如何在FastAPI的帮助下维护和设计它们以满足较高的编程标准。

What you will learn

• Explore the basics of modern Python and async I/O programming
• 掌握FastAPI框架的基本和高级概念
• Implement a FastAPI dependency to efficiently run a machine learning model
• 在FastAPI后端集成简单的人脸检测算法
• 在Web后端集成常用的Python数据科学库
• 为数据科学应用程序部署高性能且可靠的Web后端

这本书是为谁而写的

这本Python数据科学书籍面向对获取FastAPI及其生态系统知识感兴趣的数据科学家和软件开发人员，以构建数据科学应用程序。介绍了数据科学和机器学习概念的基本知识，以及如何将它们应用到Python中。

目录表

1. Python Development Environment Setup
2. Python编程的特殊性
3. Developing RESTful API with FastAPI
4. 在FastAPI中管理僵化数据模型
5. FastAPI中的依赖项注入
6. 数据库和异步ORM
7. 在FastAPI中管理身份验证和安全
8. 在FastAPI中定义双向交互通信的WebSocket
9. 使用PYTEST和HTTPX异步测试API
10. 部署FastAPI项目
11. Introduction to NumPy and Pandas
12. 使用SCRICKET-LEARN训练机器学习模型
13. 使用FastAPI创建高效的预测API端点
14. 利用WebSockets结合FastAPI和OpenCV实现实时人脸检测系统