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020			_a9798868810220
082			_a006.312 _bPRA
100			_aPrasad, Aditya Nandan _923782
245			_aIntroduction to data governance for machine learning systems: _bfundamental principles, critical practices, and future trends
260			_bSpringer _aNew York _c2024
300			_axxv, 966 p.
365			_aEUR _b37.99
500			_aTable of contents: Table of contents (10 chapters) Front Matter Pages i-xxv Download chapter PDF 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]
520			_aThis 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)
650			_aMachine learning
650			_aData science
650			_aData governance _923783
942			_cBK _2ddc
999			_c9762 _d9762