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The Machine Learning Solutions Architect Handbook - Second Edition

You're reading from  The Machine Learning Solutions Architect Handbook - Second Edition

Product type Book
Published in Apr 2024
Publisher Packt
ISBN-13 9781805122500
Pages 602 pages
Edition 2nd Edition
Languages
Author (1):
David Ping David Ping
Profile icon David Ping

Table of Contents (19) Chapters

Preface Navigating the ML Lifecycle with ML Solutions Architecture Exploring ML Business Use Cases Exploring ML Algorithms Data Management for ML Exploring Open-Source ML Libraries Kubernetes Container Orchestration Infrastructure Management Open-Source ML Platforms Building a Data Science Environment Using AWS ML Services Designing an Enterprise ML Architecture with AWS ML Services Advanced ML Engineering Building ML Solutions with AWS AI Services AI Risk Management Bias, Explainability, Privacy, and Adversarial Attacks Charting the Course of Your ML Journey Navigating the Generative AI Project Lifecycle Designing Generative AI Platforms and Solutions Other Books You May Enjoy
Index

Designing ML platforms with governance and risk management considerations

ML technology systems play a crucial role in the AI risk management process and activities. To begin with, these systems must be developed and constructed to comply with both internal and external policies and guidelines. Additionally, technology can aid in streamlining and automating ML governance procedures. The following figure illustrates the different ML governance touchpoints in an enterprise ML platform. It is important to know that ML technology alone can only help address a subset of AI risks; other enterprise security technology needs to be incorporated to form a more comprehensive governance and defense mechanism.

Figure 12.2: ML platform and ML governance

In the preceding figure, the ML governance touchpoints have been integrated into the MLOps architecture depicted in Figure 9.4 of Chapter 9, Designing an Enterprise ML Architecture with AWS ML Services.

When an ML platform...

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