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Azure Machine Learning Engineering

You're reading from   Azure Machine Learning Engineering Deploy, fine-tune, and optimize ML models using Microsoft Azure

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Product type Paperback
Published in Jan 2023
Publisher Packt
ISBN-13 9781803239309
Length 362 pages
Edition 1st Edition
Tools
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Authors (4):
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Balamurugan Balakreshnan Balamurugan Balakreshnan
Author Profile Icon Balamurugan Balakreshnan
Balamurugan Balakreshnan
Dennis Michael Sawyers Dennis Michael Sawyers
Author Profile Icon Dennis Michael Sawyers
Dennis Michael Sawyers
Sina Fakhraee Ph.D Sina Fakhraee Ph.D
Author Profile Icon Sina Fakhraee Ph.D
Sina Fakhraee Ph.D
Megan Masanz Megan Masanz
Author Profile Icon Megan Masanz
Megan Masanz
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Toc

Table of Contents (17) Chapters Close

Preface 1. Part 1: Training and Tuning Models with the Azure Machine Learning Service
2. Chapter 1: Introducing the Azure Machine Learning Service FREE CHAPTER 3. Chapter 2: Working with Data in AMLS 4. Chapter 3: Training Machine Learning Models in AMLS 5. Chapter 4: Tuning Your Models with AMLS 6. Chapter 5: Azure Automated Machine Learning 7. Part 2: Deploying and Explaining Models in AMLS
8. Chapter 6: Deploying ML Models for Real-Time Inferencing 9. Chapter 7: Deploying ML Models for Batch Scoring 10. Chapter 8: Responsible AI 11. Chapter 9: Productionizing Your Workload with MLOps 12. Part 3: Productionizing Your Workload with MLOps
13. Chapter 10: Using Deep Learning in Azure Machine Learning 14. Chapter 11: Using Distributed Training in AMLS 15. Index 16. Other Books You May Enjoy

Responsible AI principles

As mentioned, there are six core principles – Fairness, Reliability and Safety, Privacy and Security, Inclusiveness, Transparency, and Accountability – that Microsoft has incorporated into their Responsible AI Toolbox. We will briefly explain these as follows:

  • Fairness in the context of AI systems is a sociotechnical challenge that scientists and developers need to address to ensure that people are treated equally and to reduce unfairness relating to the specific use case we are building the model for. In a variety of domains and use cases, AI systems can be used to provide resources and opportunities, and through checking fairness we can ensure that we are not reinforcing existing stereotypes.

For example, if we are predicting what ethnic groups there are in a certain segment of the population and what their food preferences are, not all customer segments of cities or states have the same ethnic diversity. So, when we design the...

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