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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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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 have learned the key principles of testing and security by design. We explored the various methods to test ML solutions in order to secure them. For a comprehensive understanding and hands-on experience, implementation was done to load test our previously deployed ML model (from Chapter 7, Building Robust CI and CD Pipelines) to predict the weather. With this, you are ready to handle the diverse testing and security scenarios that will be channeled your way.

In the next chapter, we will delve into the secrets of deploying and maintaining robust ML services in production. This will enable you to deploy robust ML solutions in production. Let's delve into it.

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