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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

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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