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

Product type Paperback

Published in Apr 2021

Publisher Packt

ISBN-13 9781800562882

Length 370 pages

Edition 1st Edition

Tools

Azure Functions

Concepts

Machine Learning

Author (1):

Emmanuel Raj

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Table of Contents (18) Chapters

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

Securing your ML solution by design

Securing your ML applications is more important than ever due to the growing adoption of AI to provide smart applications. Designing and developing ML systems without keeping security in mind can be costly in terms of exposing the system to hackers, leading to manipulation, data breaches, and non-compliance. Robustness and security play an important role in ensuring an AI system is trustworthy. To build trustworthy ML applications, keeping security in mind is vital to not leave any stones unturned.

Figure 9.8 shows a framework for creating secure ML applications by design. The framework addresses key areas in the ML life cycle, ensuring confidentiality, integrity, and availability within those specific stages. Let's reflect upon each area of the ML life cycle and address the issues of confidentiality, integrity, and availability in each area:

Figure 9.8 – Framework for securing the ML life cycle by design

Let...