Introduction to Data Governance for Machine Learning Systems: Fundamental Principles, Critical Practices, and Future Trends
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, ada...
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Berkeley, CA
Apress
2024
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| Ausgabe: | First Edition |
| Schlagworte: | |
| Zusammenfassung: | 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. |
| Beschreibung: | Approx. 180 p. - 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.What You Will Learn- Comprehensive understanding of machine learning and data governance, including fundamental principles, critical practices, and emerging challenges- Navigating the complexities of managing data effectively within the context of machine learning projects- Pra Chapter 1: Introduction to Machine Learning Data Governance.- Chapter 2: Establishing a Data Governance Framework.- Chapter 3: Data Quality and Preprocessing.- Chapter.- 4: Data Privacy and Security Considerations.- Chapter 5: Ethical Implications and Bias Mitigation.- Chapter 6: Model Transparency and Interpretability.- Chapter 7: Monitoring and Maintaining Machine Learning System.- Chapter 8: Regulatory Compliance and Risk Management.- Chapter 9: Organizational Culture and Change Management.- Chapter 10: Future Trends and Emerging Challenges. |
| Beschreibung: | 180 Seiten 254 mm |
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| 500 | |a Approx. 180 p. - 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.What You Will Learn- Comprehensive understanding of machine learning and data governance, including fundamental principles, critical practices, and emerging challenges- Navigating the complexities of managing data effectively within the context of machine learning projects- Pra | ||
| 500 | |a Chapter 1: Introduction to Machine Learning Data Governance.- Chapter 2: Establishing a Data Governance Framework.- Chapter 3: Data Quality and Preprocessing.- Chapter.- 4: Data Privacy and Security Considerations.- Chapter 5: Ethical Implications and Bias Mitigation.- Chapter 6: Model Transparency and Interpretability.- Chapter 7: Monitoring and Maintaining Machine Learning System.- Chapter 8: Regulatory Compliance and Risk Management.- Chapter 9: Organizational Culture and Change Management.- Chapter 10: Future Trends and Emerging Challenges. | ||
| 520 | |a 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. | ||
| 520 | |a 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. | ||
| 520 | |a 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. | ||
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Datensatz im Suchindex
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| illustrated | Not Illustrated |
| indexdate | 2025-01-17T23:00:12Z |
| institution | BVB |
| language | English |
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| physical | 180 Seiten 254 mm |
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| spelling | Nandan Prasad, Aditya Verfasser aut Introduction to Data Governance for Machine Learning Systems Fundamental Principles, Critical Practices, and Future Trends First Edition Berkeley, CA Apress 2024 180 Seiten 254 mm txt rdacontent n rdamedia nc rdacarrier Approx. 180 p. - 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.What You Will Learn- Comprehensive understanding of machine learning and data governance, including fundamental principles, critical practices, and emerging challenges- Navigating the complexities of managing data effectively within the context of machine learning projects- Pra Chapter 1: Introduction to Machine Learning Data Governance.- Chapter 2: Establishing a Data Governance Framework.- Chapter 3: Data Quality and Preprocessing.- Chapter.- 4: Data Privacy and Security Considerations.- Chapter 5: Ethical Implications and Bias Mitigation.- Chapter 6: Model Transparency and Interpretability.- Chapter 7: Monitoring and Maintaining Machine Learning System.- Chapter 8: Regulatory Compliance and Risk Management.- Chapter 9: Organizational Culture and Change Management.- Chapter 10: Future Trends and Emerging Challenges. 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. bicssc bisacsh Database management Data mining Artificial intelligence—Data processing Artificial intelligence Machine learning Hardcover, Softcover / Informatik, EDV/Informatik |
| spellingShingle | Nandan Prasad, Aditya Introduction to Data Governance for Machine Learning Systems Fundamental Principles, Critical Practices, and Future Trends bicssc bisacsh Database management Data mining Artificial intelligence—Data processing Artificial intelligence Machine learning |
| title | Introduction to Data Governance for Machine Learning Systems Fundamental Principles, Critical Practices, and Future Trends |
| title_auth | Introduction to Data Governance for Machine Learning Systems Fundamental Principles, Critical Practices, and Future Trends |
| title_exact_search | Introduction to Data Governance for Machine Learning Systems Fundamental Principles, Critical Practices, and Future Trends |
| title_full | Introduction to Data Governance for Machine Learning Systems Fundamental Principles, Critical Practices, and Future Trends |
| title_fullStr | Introduction to Data Governance for Machine Learning Systems Fundamental Principles, Critical Practices, and Future Trends |
| title_full_unstemmed | Introduction to Data Governance for Machine Learning Systems Fundamental Principles, Critical Practices, and Future Trends |
| title_short | Introduction to Data Governance for Machine Learning Systems |
| title_sort | introduction to data governance for machine learning systems fundamental principles critical practices and future trends |
| title_sub | Fundamental Principles, Critical Practices, and Future Trends |
| topic | bicssc bisacsh Database management Data mining Artificial intelligence—Data processing Artificial intelligence Machine learning |
| topic_facet | bicssc bisacsh Database management Data mining Artificial intelligence—Data processing Artificial intelligence Machine learning |
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