You're reading from The Machine Learning Solutions Architect Handbook

Product type Book

Published in Jan 2022

Publisher Packt

ISBN-13 9781801072168

Pages 442 pages

Edition 1st Edition

Languages

Python

Concepts

Machine Learning

Author (1):

David Ping

Table of Contents (17) Chapters

Preface

1. Section 1: Solving Business Challenges with Machine Learning Solution Architecture

2. Chapter 1: Machine Learning and Machine Learning Solutions Architecture

3. Chapter 2: Business Use Cases for Machine Learning

4. Section 2: The Science, Tools, and Infrastructure Platform for Machine Learning

5. Chapter 3: Machine Learning Algorithms

6. Chapter 4: Data Management for Machine Learning

7. Chapter 5: Open Source Machine Learning Libraries

8. Chapter 6: Kubernetes Container Orchestration Infrastructure Management

9. Section 3: Technical Architecture Design and Regulatory Considerations for Enterprise ML Platforms

10. Chapter 7: Open Source Machine Learning Platforms

11. Chapter 8: Building a Data Science Environment Using AWS ML Services

12. Chapter 9: Building an Enterprise ML Architecture with AWS ML Services

13. Chapter 10: Advanced ML Engineering

14. Chapter 11: ML Governance, Bias, Explainability, and Privacy

15. Chapter 12: Building ML Solutions with AWS AI Services

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Designing an ML platform for governance

ML technology systems are critical in the overall operations of ML governance processes and activities. First, these technology systems need to be designed and built to meet the internal and external policies and guidelines themselves. Second, technology can help with simplifying and automating ML governance activities. The following diagram shows the various ML governance touchpoints in an enterprise ML platform:

Figure 11.1 – ML platform and ML governance

When an ML platform is built with ML governance in mind, it can capture and supply information to help with the three lines of defense and let you streamline the model risk management workflows. The types of tools that are used for ML governance include online data stores, workflow applications, document sharing systems, and model inventory databases. Now, let's take a closer look at some of the core ML governance components and where an ML platform...