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Building Data Science Applications with FastAPI

Building Data Science Applications with FastAPI

By : Voron
4.7 (16)
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Building Data Science Applications with FastAPI

Building Data Science Applications with FastAPI

4.7 (16)
By: Voron

Overview of this book

FastAPI is a web framework for building APIs with Python 3.6 and its later versions based on standard Python-type hints. With this book, you’ll be able to create fast and reliable data science API backends using practical examples. This book starts with the basics of the FastAPI framework and associated modern Python programming language concepts. You'll be taken through all the aspects of the framework, including its powerful dependency injection system and how you can use it to communicate with databases, implement authentication and integrate machine learning models. Later, you’ll cover best practices relating to testing and deployment to run a high-quality and robust application. You’ll also be introduced to the extensive ecosystem of Python data science packages. As you progress, you’ll learn how to build data science applications in Python using FastAPI. The book also demonstrates how to develop fast and efficient machine learning prediction backends and test them to achieve the best performance. Finally, you’ll see how to implement a real-time face detection system using WebSockets and a web browser as a client. By the end of this FastAPI book, you’ll have not only learned how to implement Python in data science projects but also how to maintain and design them to meet high programming standards with the help of FastAPI.
Table of Contents (19 chapters)
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1
Section 1: Introduction to Python and FastAPI
7
Section 2: Build and Deploy a Complete Web Backend with FastAPI
13
Section 3: Build a Data Science API with Python and FastAPI

Summary

Your application is now live on the web! In this chapter, we covered the best practices to apply before deploying your application to production: use environment variables to set configuration options, such as database URLs, and manage your Python dependencies with a requirements.txt file. Then, we showed you how to deploy your application to a serverless platform, which handles everything for you by retrieving your source code, packaging it with its dependencies, and serving it on the web. Next, you learned how to build a Docker image for FastAPI using the base image created by the creator of FastAPI. As you've seen, it allows you to be flexible while configuring the system, but you can still deploy it in a few minutes with a serverless platform that's compatible with containers. Finally, we provided you with some guidelines for manual deployment on a traditional Linux server.

This marks the end of the second part of this book. You should now be confident in writing...

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