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Building Data Science Applications with FastAPI

You're reading from   Building Data Science Applications with FastAPI Develop, manage, and deploy efficient machine learning applications with Python

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Product type Paperback
Published in Jul 2023
Publisher Packt
ISBN-13 9781837632749
Length 422 pages
Edition 2nd Edition
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Author (1):
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François Voron François Voron
Author Profile Icon François Voron
François Voron
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Table of Contents (21) Chapters Close

Preface 1. Part 1: Introduction to Python and FastAPI
2. Chapter 1: Python Development Environment Setup FREE CHAPTER 3. Chapter 2: Python Programming Specificities 4. Chapter 3: Developing a RESTful API with FastAPI 5. Chapter 4: Managing Pydantic Data Models in FastAPI 6. Chapter 5: Dependency Injection in FastAPI 7. Part 2: Building and Deploying a Complete Web Backend with FastAPI
8. Chapter 6: Databases and Asynchronous ORMs 9. Chapter 7: Managing Authentication and Security in FastAPI 10. Chapter 8: Defining WebSockets for Two-Way Interactive Communication in FastAPI 11. Chapter 9: Testing an API Asynchronously with pytest and HTTPX 12. Chapter 10: Deploying a FastAPI Project 13. Part 3: Building Resilient and Distributed Data Science Systems with FastAPI
14. Chapter 11: Introduction to Data Science in Python 15. Chapter 12: Creating an Efficient Prediction API Endpoint with FastAPI 16. Chapter 13: Implementing a Real-Time Object Detection System Using WebSockets with FastAPI 17. Chapter 14: Creating a Distributed Text-to-Image AI System Using the Stable Diffusion Model 18. Chapter 15: Monitoring the Health and Performance of a Data Science System 19. Index 20. Other Books You May Enjoy

Setting and using environment variables

Before deep-diving into the different deployment techniques, we need to structure our application to enable reliable, fast, and secure deployments. One of the key things in this process is handling configuration variables: a database URL, an external API token, a debug flag, and so on. When handling those variables, it’s necessary to handle them dynamically instead of hardcoding them into your source code. Why?

First of all, those variables will likely be different in your local environment and in production. Typically, your database URL will point to a local database on your computer while developing but will point to a proper production database in production. This is even more pertinent if you want to have other environments such as a staging or pre-production environment. Furthermore, if we need to change one of the values, we’ll have to change the code, commit it, and deploy it again. Thus, we need a convenient mechanism...

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