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

Summary

Awesome! You may not have realized it yet, but in this chapter, you learned how to architect and implement a very complex machine learning system that could rival existing image-generation services you see out there. The concepts we showed here are essential and are at the heart of all the distributed systems you could imagine, whether they are designed to run machine learning models, extraction pipelines, or math computations. By using modern tools such as FastAPI and Dramatiq, you’ll be able to implement this kind of architecture in a short time with a minimum amount of code, leading to a very quick and robust result.

We’re near the end of our journey. Before letting you live your own adventures with FastAPI, we’ll study one last important aspect when building data science applications: logging and monitoring.