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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

Chapter 9: Testing and Securing Your ML Solution

In this chapter, we will delve into Machine Learning (ML) solution testing and security aspects. You can expect to get a primer on various types of tests to test the robustness and scalability of your ML solution, as well as the knowledge required to secure your ML solution. We will look into multiple attacks on ML solutions and ways to defend your ML solution.

In this chapter, we will be learning with examples as we perform load testing and security testing for the business use case of weather prediction we have been previously working on. We will start by reflecting on the need for testing and securing your ML solution and go on to explore the other following topics in the chapter:

  • Understanding the need for testing and securing your ML application
  • Testing your ML solution by design
  • Securing your ML solution by design
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