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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

Testing an API Asynchronously with pytest and HTTPX

In software development, a significant part of the developer’s work should be dedicated to writing tests. At first, you may be tempted to manually test your application by running it, making a few requests, and arbitrarily deciding that “everything works.” However, this approach is flawed and can’t guarantee that your program works in every circumstance and that you didn’t break things along the way.

That’s why several disciplines have emerged regarding software testing: unit tests, integration tests, end-to-end tests, acceptance tests, and others. These techniques aim to validate the functionality of software from a micro level, where we test single functions (unit tests), to a macro level, where we test a global feature that delivers value to the user (acceptance tests). In this chapter, we’ll focus on the first level: unit testing.

Unit tests are short programs designed to...

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