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

Deploying a FastAPI application with Docker

Docker is a widely used technology for containerization. Containers are small, self-contained systems running on a computer. Each container contains all the files and configurations necessary for running a single application: a web server, a database engine, a data processing application, and so on. The main goal is to be able to run those applications without worrying about the dependency and version conflicts that often happen when trying to install and configure them on the system.

Besides, Docker containers are designed to be portable and reproducible: to create a Docker container, you simply have to write a Dockerfile containing all the necessary instructions to build the small system, along with all the files and configuration you need. Those instructions are executed during a build, which results in a Docker image. This image is a package containing your small system, ready to use, which you can easily share on the internet through...