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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

Deploying a FastAPI application on a serverless platform

In recent years, serverless platforms have gained a lot of popularity and have become a very common way to deploy web applications. Those platforms completely hide the complexity of setting up and managing a server, giving you the tools to automatically build and deploy your application in minutes. Google App Engine, Heroku, and Azure App Service are among the most popular. Even though they have their own specificities, all these serverless platforms work on the same principles. This is why, in this section, we’ll outline the common steps you should follow.

Usually, serverless platforms expect you to provide the source code in the form of a GitHub repository, which you push directly to their servers or which they pull automatically from GitHub. Here, we’ll assume that you have a GitHub repository with the source code structured like so:

Figure 10.1 – Project structure for serverless deployment

Figure 10.1 – Project structure for serverless...

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Building Data Science Applications with FastAPI
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