Introduction To Data Governance For Machine Learning Systems
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Introduction to Data Governance for Machine Learning Systems
Author: Aditya Nandan Prasad
language: en
Publisher: Springer Nature
Release Date: 2024-12-13
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 Practical strategies and best practices for implementing effective data governance in machine learning projects Key aspects such as data quality, privacy, security, and ethical considerations, ensuring responsible and effective use of data Preparation for the evolving landscape of ML data governance with a focus on future trends and emerging challenges in the rapidly evolving field of AI and machine learning Who This Book Is For Data professionals, including data scientists, data engineers, AI developers, or data governance specialists, as well as managers or decision makers looking to implement or improve data governance practices for machine learning projects
Introduction to Data Governance for Machine Learning Systems
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 Practical strategies and best practices for implementing effective data governance in machine learning projects Key aspects such as data quality, privacy, security, and ethical considerations, ensuring responsible and effective use of data Preparation for the evolving landscape of ML data governance with a focus on future trends and emerging challenges in the rapidly evolving field of AI and machine learning Who This Book Is For Data professionals, including data scientists, data engineers, AI developers, or data governance specialists, as well as managers or decision makers looking to implement or improve data governance practices for machine learning projects
Data Governance, DevSecOps, and Advancements in Modern Software
Author: Elbaghazaoui, Bahaa Eddine
language: en
Publisher: IGI Global
Release Date: 2025-04-24
In today’s digital landscape, data governance, DevSecOps, and advancements in modern software development have become critical in secure and efficient technology ecosystems. As organizations rely on large amounts of data and sophisticated software systems to drive innovation and business success, the need for improved frameworks to manage, protect, and optimize this data increases. Data governance ensures data is accurate, secure, and compliant with regulations, while DevSecOps, an integrated approach to development, security, and operations, empowers teams to build, test, and utilize software with security embedded through its lifecycle. Along with the latest advancements in modern software technologies, these concepts form the foundation for building resilient, secure, and scalable applications. The intersection of these practices shapes the future of how software is developed, deployed, and governed, and further research may provide both opportunities and challenges for connection. Data Governance, DevSecOps, and Advancements in Modern Software explores the integration of key technologies and methodologies that define the modern digital landscape, with a focus on DataOps, DevSecOps, data governance, and software architecture. It provides a comprehensive guide to managing data workflows and enhancing operational efficiency while embedding security at every stage of the development lifecycle. This book covers topics such as data science, artificial intelligence, and resilient systems, and is a useful resource for data scientists, engineers, software developers, business owners, researchers, and academicians.