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

The regulatory landscape around AI risk management

With the fast advancement of AI technologies and adoption in critical business decision-making, and the negative impacts that AI systems can potentially have on individuals, organizations, and societies, many countries and jurisdictions have established policies, guidance, and regulations to help manage the risks of AI adoption. It is also expected that more and more legislation will be proposed and passed by different countries and jurisdictions at a fast rate.

In the United States (US), the Federal Reserve and the Office of the Comptroller of the Currency (OCC) published the Supervisory Guidance on Model Risk Management (OCC 2011-2012/SR 11-7) as early as 2011. SR 11-7 has become the key regulatory guidance for model risk management in the US. This guidance establishes the main principles for model risk management covering governance, policies and controls, model development, implementation and use, and model validation processes...