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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

Basics of Python programming

First of all, let’s review some of the key aspects of Python:

  • It’s an interpreted language. Contrary to languages such as C or Java, it doesn’t need to be compiled, which allows us to run Python code interactively.
  • It’s dynamically typed. The type of values is determined at runtime.
  • It supports several programming paradigms: procedural, object-oriented, and functional programming.

This makes Python quite a versatile language, from simple automation scripts to complex data science projects.

Let’s now write and run some Python!

Running Python scripts

As we said, Python is an interpreted language. Hence, the simplest and quickest way to run some Python code is to launch an interactive shell. Just run the following command to start a session:

$ pythonPython 3.10.8 (main, Nov    8 2022, 08:55:03) [Clang 14.0.0 (clang-1400.0.29.202)] on darwin
Type "help", &quot...
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Building Data Science Applications with FastAPI
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