Book Image

Building Data Science Applications with FastAPI - Second Edition

By : François Voron
3 (1)
Book Image

Building Data Science Applications with FastAPI - Second Edition

3 (1)
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)
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

Type hinting and type checking with mypy

In the first section of this chapter, we said that Python was a dynamically typed language: the interpreter doesn’t check types at compile time but rather at runtime. This makes the language a bit more flexible and the developer a bit more efficient. However, if you are experienced with that kind of language, you probably know that it’s easy to produce errors and bugs in this context: forgetting arguments, type mismatches, and so on.

This is why Python introduced type hinting starting in version 3.5. The goal is to provide a syntax to annotate the source code with type annotations: each variable, function, and class can be annotated to give indications about the types they expect. This doesn’t mean that Python becomes a statically typed language. Those annotations remain completely optional and are ignored by the interpreter. However, those annotations can be used by static type checkers, which will check whether your...