Building Data Science Applications with FastAPI - Develop, manage, and deploy...

[Farid-Khan]

| 423 |​

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.
Table of Contents

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



Contents of Download:
Building Data Science Applications with FastAPI.pdf (4.66 MB)


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