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Building Data Science Applications with FastAPI

Building Data Science Applications with FastAPI - Second Edition

By : Voron
4.2 (9)
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Building Data Science Applications with FastAPI

Building Data Science Applications with FastAPI

4.2 (9)
By: 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)
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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 Project

Building a good application is great, but it’s even better if customers can enjoy it. In this chapter, you’ll look at different techniques and the best practices for deploying your FastAPI application to make it available on the web. First, you’ll learn how to structure your project to make it ready for deployment by using environment variables to set the configuration options you need, as well as by managing your dependencies properly with pip. Once that’s done, we’ll show you three ways to deploy your application: with a serverless cloud platform, with a Docker container, and with a traditional Linux server.

In this chapter, we’re going to cover the following main topics:

  • Setting and using environment variables
  • Managing Python dependencies
  • Deploying a FastAPI application on a serverless platform
  • Deploying a FastAPI application with Docker
  • Deploying a FastAPI application on a traditional...
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Building Data Science Applications with FastAPI
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