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

Summary

Well done! You’re now acquainted with all the basic features of FastAPI. Throughout this chapter, you learned how to create and run API endpoints where you can validate and retrieve data from all parts of an HTTP request: the path, the query, the parameters, the headers, and, of course, the body. You also learned how to tailor the HTTP response to your needs, whether it is a simple JSON response, an error, or a file to download. Finally, you looked at how to define separate API routers and include them in your main application to keep a clean and maintainable project structure.

You have enough knowledge now to start building your own API with FastAPI. In the next chapter, we’ll focus on Pydantic models. You now know that they are at the core of the data validation features of FastAPI, so it’s crucial to fully understand how they work and how to manipulate them efficiently.