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Introduction to Data Governance for Machine Learning Systems: Fundamental Principles, Critical Practices, and Future Trends
book

Introduction to Data Governance for Machine Learning Systems: Fundamental Principles, Critical Practices, and Future Trends

by Aditya Nandan Prasad
December 2024
Intermediate to advanced
984 pages
27h 1m
English
Apress
Content preview from Introduction to Data Governance for Machine Learning Systems: Fundamental Principles, Critical Practices, and Future Trends
© The Author(s), under exclusive license to APress Media, LLC, part of Springer Nature 2024
A. Nandan PrasadIntroduction to Data Governance for Machine Learning Systemshttps://doi.org/10.1007/979-8-8688-1023-7_8

8. Regulatory Compliance and Risk Management

Aditya Nandan Prasad1  
(1)
Bangalore, Karnataka, India
 

Introduction to Regulatory Compliance for Machine Learning

Regulatory compliance in the context of machine learning (ML) encompasses adhering to laws, standards, and guidelines designed to ensure that ML systems operate within legal and ethical boundaries. This compliance is critical due to the complex nature of ML systems, which involve processing vast amounts of data and making automated decisions that can significantly impact individuals ...

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Publisher Resources

ISBN: 9798868810237Purchase LinkPublisher Website