Book Image

Building Data Science Applications with FastAPI - Second Edition

By : François Voron
3 (1)
Book Image

Building Data Science Applications with FastAPI - Second Edition

3 (1)
By: François Voron

Overview of this book

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.
Table of Contents (21 chapters)
1
Part 1: Introduction to Python and FastAPI
7
Part 2: Building and Deploying a Complete Web Backend with FastAPI
13
Part 3: Building Resilient and Distributed Data Science Systems with FastAPI

Part 3: Building Resilient and Distributed Data Science Systems with FastAPI

This part will introduce you to the basic concepts of data science and machine learning, as well as the most popular Python tools and libraries for those tasks. We’ll see how to integrate those tools into a FastAPI backend and how to build a distributed system to perform resource-intensive tasks in a scalable way.

This section comprises the following chapters:

  • Chapter 11, Introduction to Data Science in Python
  • Chapter 12, Creating an Efficient Prediction API Endpoint with FastAPI
  • Chapter 13, Implementing a Real-Time Object Detection System Using WebSockets with FastAPI
  • Chapter 14, Creating a Distributed Text-to-Image AI System Using the Stable Diffusion Model
  • Chapter 15, Monitoring the Health and Performance of a Data Science System