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Engineering MLOps

You're reading from   Engineering MLOps Rapidly build, test, and manage production-ready machine learning life cycles at scale

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Product type Paperback
Published in Apr 2021
Publisher Packt
ISBN-13 9781800562882
Length 370 pages
Edition 1st Edition
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Author (1):
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Emmanuel Raj Emmanuel Raj
Author Profile Icon Emmanuel Raj
Emmanuel Raj
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Toc

Table of Contents (18) Chapters Close

Preface 1. Section 1: Framework for Building Machine Learning Models
2. Chapter 1: Fundamentals of an MLOps Workflow FREE CHAPTER 3. Chapter 2: Characterizing Your Machine Learning Problem 4. Chapter 3: Code Meets Data 5. Chapter 4: Machine Learning Pipelines 6. Chapter 5: Model Evaluation and Packaging 7. Section 2: Deploying Machine Learning Models at Scale
8. Chapter 6: Key Principles for Deploying Your ML System 9. Chapter 7: Building Robust CI/CD Pipelines 10. Chapter 8: APIs and Microservice Management 11. Chapter 9: Testing and Securing Your ML Solution 12. Chapter 10: Essentials of Production Release 13. Section 3: Monitoring Machine Learning Models in Production
14. Chapter 11: Key Principles for Monitoring Your ML System 15. Chapter 12: Model Serving and Monitoring 16. Chapter 13: Governing the ML System for Continual Learning 17. Other Books You May Enjoy

Summary

In this chapter, we learned about the key principles of continual learning in ML solutions. We learned about Explainable Monitoring (the governance component) by implementing hands-on error handling and configuring actions to alert developers of ML systems using email notifications. Lastly, we looked at ways to enable model retraining and how to maintain the CI/CD pipeline. With this, you have been equipped with the critical skills to automate and govern MLOps for your use cases.

Congratulations on finishing this book! The world of MLOps is constantly evolving for the better. You are now equipped to help your business thrive using MLOps. I hope you enjoyed reading and learning by completing the hands-on MLOps implementations. Go out there and be the change you wish to see. All the best with your MLOps endeavors!

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