MARC details
| 000 -LEADER |
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| fixed length control field |
03355nam a22002177a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20250505113850.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
250505b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9798868810220 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
006.312 |
| Item number |
PRA |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Prasad, Aditya Nandan |
| 245 ## - TITLE STATEMENT |
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| Title |
Introduction to data governance for machine learning systems: |
| Remainder of title |
fundamental principles, critical practices, and future trends |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Name of publisher, distributor, etc. |
Springer |
| Place of publication, distribution, etc. |
New York |
| Date of publication, distribution, etc. |
2024 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xxv, 966 p. |
| 365 ## - TRADE PRICE |
|---|
| Price type code |
EUR |
| Price amount |
37.99 |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Table of contents:<br/>Table of contents (10 chapters)<br/>Front Matter<br/>Pages i-xxv<br/>Download chapter PDF <br/>Introduction to Machine Learning Data Governance<br/>Aditya Nandan Prasad<br/>Pages 1-48<br/>Establishing a Data Governance Framework<br/>Aditya Nandan Prasad<br/>Pages 49-108<br/>Data Quality and Preprocessing<br/>Aditya Nandan Prasad<br/>Pages 109-223<br/>Data Privacy and Security Considerations<br/>Aditya Nandan Prasad<br/>Pages 225-305<br/>Ethical Implications and Bias Mitigation<br/>Aditya Nandan Prasad<br/>Pages 307-382<br/>Model Transparency and Interpretability<br/>Aditya Nandan Prasad<br/>Pages 383-428<br/>Monitoring and Maintaining Machine Learning Systems<br/>Aditya Nandan Prasad<br/>Pages 429-483<br/>Regulatory Compliance and Risk Management<br/>Aditya Nandan Prasad<br/>Pages 485-624<br/>Organizational Culture and Change Management<br/>Aditya Nandan Prasad<br/>Pages 625-678<br/>Future Trends and Emerging Challenges<br/>Aditya Nandan Prasad<br/>Pages 679-710<br/>[https://link.springer.com/book/10.1007/979-8-8688-1023-7] |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
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.<br/><br/>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.<br/><br/>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.<br/><br/> <br/>(https://link.springer.com/book/10.1007/979-8-8688-1023-7) |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Machine learning |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Data science |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Data governance |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Koha item type |
Book |
| Source of classification or shelving scheme |
Dewey Decimal Classification |
