Introduction to data governance for machine learning systems: fundamental principles, critical practices, and future trends

Table of contents:
Table of contents (10 chapters)
Front Matter
Pages i-xxv
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Introduction to Machine Learning Data Governance
Aditya Nandan Prasad
Pages 1-48
Establishing a Data Governance Framework
Aditya Nandan Prasad
Pages 49-108
Data Quality and Preprocessing
Aditya Nandan Prasad
Pages 109-223
Data Privacy and Security Considerations
Aditya Nandan Prasad
Pages 225-305
Ethical Implications and Bias Mitigation
Aditya Nandan Prasad
Pages 307-382
Model Transparency and Interpretability
Aditya Nandan Prasad
Pages 383-428
Monitoring and Maintaining Machine Learning Systems
Aditya Nandan Prasad
Pages 429-483
Regulatory Compliance and Risk Management
Aditya Nandan Prasad
Pages 485-624
Organizational Culture and Change Management
Aditya Nandan Prasad
Pages 625-678
Future Trends and Emerging Challenges
Aditya Nandan Prasad
Pages 679-710
[https://link.springer.com/book/10.1007/979-8-8688-1023-7]

This book is the first comprehensive guide to the intersection of data governance and machine learning (ML) projects. As ML applications proliferate, the quality, reliability, and ethical use of data is central to their success, which gives ML data governance unprecedented significance. However, adapting data governance principles to ML systems presents unique, complex challenges. Author Aditya Nandan Prasad equips you with the knowledge and tools needed to navigate this dynamic landscape effectively. Through this guide, you will learn to implement robust and responsible data governance practices, ensuring the development of sustainable, ethical, and future-proofed AI applications.

The book begins by covering fundamental principles and practices of underlying ML applications and data governance before diving into the unique challenges and opportunities at play when adapting data governance theory and practice to ML projects, including establishing governance frameworks, ensuring data quality and interpretability, preprocessing, and the ethical implications of ML algorithms and techniques, from mitigating bias in AI systems to the importance of transparency in models.

Monitoring and maintaining ML systems performance is also covered in detail, along with regulatory compliance and risk management considerations. Moreover, the book explores strategies for fostering a data-driven culture within organizations and offers guidance on change management to ensure successful adoption of data governance initiatives. Looking ahead, the book examines future trends and emerging challenges in ML data governance, such as Explainable AI (XAI) and the increasing complexity of data.


(https://link.springer.com/book/10.1007/979-8-8688-1023-7)