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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 Oct 2021
Publisher Packt
ISBN-13 9781801079211
Length 426 pages
Edition 1st 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 (19) Chapters Close

Preface 1. Section 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 Injections in FastAPI 7. Section 2: Build and Deploy 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. Section 3: Build a Data Science API with Python and FastAPI
14. Chapter 11: Introduction to NumPy and pandas 15. Chapter 12: Training Machine Learning Models with scikit-learn 16. Chapter 13: Creating an Efficient Prediction API Endpoint with FastAPI 17. Chapter 14: Implement a Real-Time Face Detection System Using WebSockets with FastAPI and OpenCV 18. Other Books You May Enjoy

Summary

In this chapter, we showed how WebSockets can help us bring a more interactive experience to users. Thanks to OpenCV, we were able to quickly implement a face detection system. Then, we integrated it into a WebSocket endpoint with the help of FastAPI. Finally, by using a modern JavaScript API, we sent video input and displayed algorithm results directly in the browser. All in all, a project like this might sound complex to make at first, but we saw that powerful tools such as FastAPI enable us to get results in a very short time and with very comprehensible source code.

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

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