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Building Data Science Applications with FastAPI

Building Data Science Applications with FastAPI - Second Edition

By : François Voron
4.3 (10)
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Building Data Science Applications with FastAPI

Building Data Science Applications with FastAPI

4.3 (10)
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)
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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

Summary

Your application is now live on the web! In this chapter, we covered the best practices to apply before deploying your application to production: use environment variables to set configuration options, such as database URLs, and manage your Python dependencies with a requirements.txt file. Then, we showed you how to deploy your application to a serverless platform, which handles everything for you by retrieving your source code, packaging it with its dependencies, and serving it on the web. Next, you learned how to build a Docker image for FastAPI using the base image created by the creator of FastAPI. As you saw, it allows you to be flexible while configuring the system, but you can still deploy it in a few minutes with a serverless platform that’s compatible with containers. Finally, we provided you with some guidelines for manual deployment on a traditional Linux server.

This marks the end of the second part of this book. You should now be confident in writing...

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Building Data Science Applications with FastAPI
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