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Building Data Science Applications with FastAPI

Building Data Science Applications with FastAPI - Second Edition

By : François Voron
4.3 (10)
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Building Data Science Applications with FastAPI

Building Data Science Applications with FastAPI

4.3 (10)
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)
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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

Summary

Congratulations! You’ve learned about another important aspect of FastAPI: designing and managing data models with Pydantic. You should now be confident about creating models and applying validation at the field level, with built-in options and types, and also by implementing your own validation methods. You also know how to apply validation at the object level to check consistency between several fields. You also learned how to leverage model inheritance to prevent code duplication and repetition while defining your model variations. Finally, you learned how to correctly work with Pydantic model instances in order to transform and update them in an efficient and readable way.

You know almost all the features of FastAPI by now. There is one last very powerful feature for you to learn about: dependency injection. This allows you to define your own logic and values and directly inject them into your path operation functions, as you do for path parameters and payload...

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