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Building Data Science Applications with FastAPI

Building Data Science Applications with FastAPI - Second Edition

By : Voron
4.2 (9)
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Building Data Science Applications with FastAPI

Building Data Science Applications with FastAPI

4.2 (9)
By: 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

Writing object-oriented programs

As we said in the first section of this chapter, Python is a multi-paradigm language, and one of those paradigms is object-oriented programming. In this section, we’ll review how you can define classes and how you can instantiate and use objects. You’ll see that Python syntax is once again very lightweight.

Defining a class

Defining a class in Python is straightforward: use the class keyword, type the name of your class, and begin a new block. You can then define methods under it just like you would for regular functions. Let’s review an example:

chapter02_classes_objects_01.py

class Greetings:        def greet(self, name):
                return f"Hello, {name}"
c = Greetings()
print(c.greet("John"))    # "Hello, John"
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Programming languages
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Building Data Science Applications with FastAPI
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