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

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

Until now, in this book, we’ve built APIs where all the operations were computed inside the request handling. Said another way, before they could get their response, the user had to wait for the server to do everything we had defined: request validation, database queries, ML predictions, and so on. However, this behavior is not always desired or possible.

A typical example is email notifications. It happens quite often in a web application that we need to send an email to the user because they just registered or they performed a specific action. To do this, the server needs to send a request to an email server so the email can be sent. This operation could take a few milliseconds. If we do this inside the request handling, the response will be delayed until we send the email. This is not a very good experience since the user doesn’t really care how and when the email is sent. This example...