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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

Summary

Congratulations! You are now able to report metrics and build your own dashboards in Grafana to monitor your data science applications. Over time, don’t hesitate to add new metrics or complete your dashboards if you notice some blind spots: the goal is to be able to watch over every important part at a glance so you can quickly take corrective actions. Those metrics can also be used to drive the evolution of your work: by monitoring the performance and accuracy of your ML models, you can track the effects of your changes and see whether you are going in the right direction.

This is the end of this book and our FastAPI journey. We sincerely hope that you liked it and that you learned a lot along the way. We’ve covered many subjects, sometimes just by scratching the surface, but you should now be ready to build your own projects with FastAPI and serve smart data science algorithms. Be sure to check all the external resources we proposed along the way, as they...

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