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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

Handling request parameters

The main goal of a representational state transfer (REST) API is to provide a structured way to interact with data. As such, it’s crucial for the end user to send some information to tailor the response they need, such as path parameters, query parameters, body payloads, headers, and so on.

Web frameworks usually ask you to manipulate a request object to retrieve the parts you are interested in and manually apply validation to handle them. However, that’s not necessary with FastAPI! Indeed, it allows you to define all of your parameters declaratively. Then, it’ll automatically retrieve them in the request and apply validation based on the type hints. This is why we introduced type hinting in Chapter 2, Python Programming Specificities: it’s used by FastAPI to perform data validation!

Next, we’ll explore how you can use this feature to retrieve and validate this input data from different parts of the request.

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