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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

Communicating with a SQL database with SQLAlchemy ORM

To begin, we’ll discuss how to work with a relational database using the SQLAlchemy library. SQLAlchemy has been around for years and is the most popular library in Python when you wish to work with SQL databases. Since version 1.4, it also natively supports async.

The key thing to understand about this library is that it’s composed of two parts:

  • SQLAlchemy Core, which provides all the fundamental features to read and write data to SQL databases
  • SQLAlchemy ORM, which provides a powerful abstraction over SQL concepts

While you can choose to only use SQLAlchemy Core, it’s generally more convenient to use ORM. The goal of ORM is to abstract away the SQL concepts of tables and columns so that you only have to deal with Python objects. The role of ORM is to map those objects to the tables and columns they belong to and generate the corresponding SQL queries automatically.

The first step is...

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