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

Understanding AI risk management

To address the various risks associated with AI and to comply with different compliance regulations, many organizations, especially in the regulated industry, have developed and implemented AI risk management programs. In short, AI risk management is the process of identifying, assessing, and mitigating the risk associated with the use of AI in automated decision-making. The ultimate goal of AI risk management is to establish trust in the AI/ML systems and ensure compliance with applicable rules and regulations.

Trusting an AI system requires rigorous assessment and consideration of the AI system across many different dimensions and criteria. Functionally, a trusted AI system needs to provide valid predictions/responses reliably for its intended use. This means that generated predictions/responses are consistently valid and can be trusted for reliable decision-making. Ethically, a trusted AI system needs to be safe to use, explainable, privacy...