You're reading from The Machine Learning Solutions Architect Handbook Practical strategies and best practices on the ML lifecycle, system design, MLOps, and generative AI

Product type Paperback

Published in Apr 2024

Publisher Packt

ISBN-13 9781805122500

Length 602 pages

Edition 2nd Edition

Languages

Python

Tools

MLOps

Concepts

Machine Learning

Author (1):

David Ping

View More author details

Table of Contents (19) Chapters

Preface

1. Navigating the ML Lifecycle with ML Solutions Architecture FREE CHAPTER

2. Exploring ML Business Use Cases

3. Exploring ML Algorithms

4. Data Management for ML

5. Exploring Open-Source ML Libraries

6. Kubernetes Container Orchestration Infrastructure Management

7. Open-Source ML Platforms

8. Building a Data Science Environment Using AWS ML Services

9. Designing an Enterprise ML Architecture with AWS ML Services

10. Advanced ML Engineering

11. Building ML Solutions with AWS AI Services

12. AI Risk Management

13. Bias, Explainability, Privacy, and Adversarial Attacks

14. Charting the Course of Your ML Journey

15. Navigating the Generative AI Project Lifecycle

16. Designing Generative AI Platforms and Solutions

17. Other Books You May Enjoy

18. Index

Summary

This chapter delved into several areas related to AI risk management and the technology platforms that support it. By now, you should have a solid understanding of the key AI-related risk scenarios, why AI risk management is critical, and how to detect and address potential risks throughout the AI lifecycle. Additionally, you should be aware of the significance of ML platforms in supporting AI risk management. It is worth noting that AI risk is a vast and complex domain with many unresolved risk challenges and new emergent risks arising rapidly. Moreover, the fast advancement in AI technology and adoption is also creating new risk exposure that risk management professionals must constantly address.

In the next chapter, we will dive deeper into several specific AI risk topics and mitigation techniques, including bias, model explainability, model robustness, and adversarial attacks.