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

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

2. 
   
3. 主要特点
4. 涵盖FastAPI框架的概念，包括与异步编程、类型提示和依赖注入有关的方面。
5. 使用现代Python为数据科学开发高效的RESTful APIs
6. 使用FastAPI构建、测试和部署高性能的数据科学和机器学习系统
7. 书中描述
8. FastAPI是一个网络框架，用于在标准Python类型提示的基础上用Python 3.6及其后续版本构建API。通过本书，你将能够利用实际的例子创建快速可靠的数据科学API后端。
9. 
   
10. 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。你将了解该框架的所有方面，包括其强大的依赖注入系统，以及你如何使用它与数据库通信、实现认证和集成机器学习模型。随后，你将学习与测试和部署有关的最佳实践，以运行一个高质量和健壮的应用程序。你还会被介绍到Python数据科学软件包的广泛生态系统。随着你的进展，你将学习如何使用FastAPI在Python中构建数据科学应用程序。本书还演示了如何开发快速有效的机器学习预测后端，并对其进行测试以达到最佳性能。最后，你将看到如何使用WebSockets和Web浏览器作为客户端来实现一个实时人脸检测系统。
11. 
    
12. 在这本FastAPI书的结尾，你不仅学会了如何在数据科学项目中实现Python，还学会了如何在FastAPI的帮助下维护和设计这些项目以满足高编程标准。
13. 
    
14. 您将学到的内容
15. 探索现代Python和异步I/O编程的基础知识
16. 掌握FastAPI框架的基本和高级概念
17. 实施一个FastAPI依赖，以有效地运行一个机器学习模型
18. 在FastAPI的后端集成一个简单的人脸检测算法
19. 在网络后端中整合常见的Python数据科学库
20. 为数据科学应用部署一个高性能和可靠的网络后端
21. 本书适用对象
22. 这本Python数据科学书适用于有兴趣获得FastAPI及其生态系统知识的数据科学家和软件开发人员，以构建数据科学应用程序。推荐使用数据科学和机器学习概念的基本知识，以及如何在Python中应用它们。
23. 
    
24. 目录
25. Python开发环境的设置
26. Python编程的特殊性
27. 使用FastAPI开发RESTful API
28. 在FastAPI中管理pydantic数据模型
29. FastAPI中的依赖注入
30. 数据库和异步ORMs
31. 在FastAPI中管理认证和安全
32. 在FastAPI中为双向互动通信定义WebSockets
33. 用pytest和HTTPX异步测试API
34. 部署一个FastAPI项目
35. NumPy和Pandas简介
36. 用scikit-learn训练机器学习模型
37. 用FastAPI创建一个高效的预测API端点
38. 使用FastAPI和OpenCV的WebSockets实现一个实时人脸检测系统