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

Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

Symbols

*args syntax

using 27, 28

**kwargs syntax

using 27, 28

== None 22

A

access token

endpoints, securing with 189

generating 184

aggregating operations, NumPy

reference link 279

Amazon ECR

reference link 260

Amazon Elastic Container Service

reference link 261

Amazon RDS

reference link 256

Amazon Web Services (AWS) 214, 344

Any annotation

using 49

apt 4

arrays

adding 278

aggregating 279

comparing 279

creating, with NumPy 272-274

manipulating 270, 271

manipulating, with NumPy 276, 277

multiplying 278

asynchronous generator 170

asynchronous I/O

working with 51-54

Asynchronous Server Gateway Interface (ASGI) 52, 57

automatic interactive documentation 57

Azure Database for PostgreSQL

reference...