Introduction to Data Governance for Machine Learning Systems: Fundamental Principles, Critical Practices, and Future Trends - Tapa blanda

Acerca del autor

Aditya Nandan Prasad is an experienced analytics leader with a strong track record in driving business intelligence and recommendations for operational and strategic decision making. He excels at leading and developing high-performing teams and collaborating to identify growth strategies. With a passion for complex data analysis and a tool-agnostic approach, he brings a data-driven perspective to solving business problems. Aditya has successfully led data migration projects and implemented innovative analytics solutions to support strategic business initiatives, and his experience in leading and collaborating with cross-functional teams has helped him become an expert on implementing data governance practices within organizations.

De la contraportada

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