Building Data Science Applications with FastAPI - Second Edition: Develop, manage, and deploy efficient machine learning applications with Python
暫譯: 使用 FastAPI 建立資料科學應用程式 - 第二版：使用 Python 開發、管理和部署高效的機器學習應用程式

Voron, François

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

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商品描述

Learn all the features and best practices of FastAPI to build, deploy, and monitor powerful data science and AI apps, like object detection or image generation.
Purchase of the print or Kindle book includes a free PDF eBook

Key Features

  • Uncover the secrets of FastAPI, including async I/O, type hinting, and dependency injection
  • Learn to add authentication, authorization, and interaction with databases in a FastAPI backend
  • Develop real-world projects using pre-trained AI models

Book Description

Building Data Science Applications with FastAPI is the go-to resource for creating efficient and dependable data science API backends. This second edition incorporates the latest Python and FastAPI advancements, along with two new AI projects – a real-time object detection system and a text-to-image generation platform using Stable Diffusion.

The book starts with the basics of FastAPI and modern Python programming. You'll grasp FastAPI's robust dependency injection system, which facilitates seamless database communication, authentication implementation, and ML model integration. As you progress, you'll learn testing and deployment best practices, guaranteeing high-quality, resilient applications.

Throughout the book, you'll build data science applications using FastAPI with the help of projects covering common AI use cases, such as object detection and text-to-image generation. These hands-on experiences will deepen your understanding of using FastAPI in real-world scenarios.

By the end of this book, you'll be well equipped to maintain, design, and monitor applications to meet the highest programming standards using FastAPI, empowering you to create fast and reliable data science API backends with ease while keeping up with the latest advancements.

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
  • Deploy a performant and reliable web backend for a data science application
  • Integrate common Python data science libraries into a web backend
  • Integrate an object detection algorithm into a FastAPI backend
  • Build a distributed text-to-image AI system with Stable Diffusion
  • Add metrics and logging and learn how to monitor them

Who this book is for

This 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.

商品描述(中文翻譯)

學習 FastAPI 的所有功能和最佳實踐，以構建、部署和監控強大的數據科學和 AI 應用程式，例如物體檢測或圖像生成。

購買印刷版或 Kindle 版書籍包括免費的 PDF 電子書

主要特點


  • 揭開 FastAPI 的秘密，包括 async I/O、類型提示和依賴注入

  • 學習如何在 FastAPI 後端添加身份驗證、授權和與數據庫的互動

  • 使用預訓練的 AI 模型開發實際項目

書籍描述

《使用 FastAPI 構建數據科學應用程式》是創建高效且可靠的數據科學 API 後端的首選資源。本書第二版納入了最新的 Python 和 FastAPI 進展，以及兩個新的 AI 項目——一個實時物體檢測系統和一個使用 Stable Diffusion 的文本到圖像生成平台。



本書從 FastAPI 和現代 Python 編程的基本概念開始。您將掌握 FastAPI 強大的依賴注入系統，這使得數據庫通信、身份驗證實現和機器學習模型集成變得無縫。隨著進展，您將學習測試和部署的最佳實踐，確保高品質和韌性的應用程式。



在整本書中，您將使用 FastAPI 構建數據科學應用程式，並通過涵蓋常見 AI 用例的項目，例如物體檢測和文本到圖像生成，獲得實踐經驗。這些實踐經歷將加深您對在現實場景中使用 FastAPI 的理解。



在本書結束時，您將能夠維護、設計和監控應用程式，以滿足使用 FastAPI 的最高編程標準，使您能夠輕鬆創建快速且可靠的數據科學 API 後端，同時跟上最新的進展。

您將學到什麼


  • 探索現代 Python 和 async I/O 編程的基本概念

  • 掌握 FastAPI 框架的基本和進階概念

  • 為數據科學應用程式部署高效且可靠的網頁後端

  • 將常見的 Python 數據科學庫整合到網頁後端中

  • 將物體檢測算法整合到 FastAPI 後端中

  • 使用 Stable Diffusion 構建分散式文本到圖像 AI 系統

  • 添加指標和日誌，並學習如何監控它們

本書適合誰

本書適合對 FastAPI 及其生態系統感興趣的數據科學家和軟體開發人員，以構建數據科學應用程式。建議具備基本的數據科學和機器學習概念知識，以及如何在 Python 中應用這些概念。

目錄大綱

  1. Python Development Environment Setup
  2. Python Programming Specificities
  3. Developing a RESTful API with FastAPI
  4. Managing Pydantic Data Models in FastAPI
  5. Dependency Injection 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 Data Science in Python
  12. Creating an Efficient Prediction API Endpoint with FastAPI
  13. Implementing a Real-Time Object Detection System Using WebSockets with FastAPI
  14. Creating a Distributed Text-to-Image AI System Using the Stable Diffusion Model
  15. Monitoring the Health and Performance of a Data Science System

目錄大綱(中文翻譯)


  1. Python Development Environment Setup

  2. Python Programming Specificities

  3. Developing a RESTful API with FastAPI

  4. Managing Pydantic Data Models in FastAPI

  5. Dependency Injection 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 Data Science in Python

  12. Creating an Efficient Prediction API Endpoint with FastAPI

  13. Implementing a Real-Time Object Detection System Using WebSockets with FastAPI

  14. Creating a Distributed Text-to-Image AI System Using the Stable Diffusion Model

  15. Monitoring the Health and Performance of a Data Science System

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