This book provides a comprehensive and up-to-date exploration of data management practices tailored to the needs and challenges of AI technologies. Aimed at masters students, postgraduate trainees, and professionals, this book guides readers through essential components of the data management process, including data collection and preprocessing, ensuring data quality, and establishing effective training and finetuning procedures for AI applications. It also covers AI output validation, compliance measures, and risk mitigation strategies to improve data usability and reliability.

This book provides a comprehensive and up-to-date exploration of data management practices tailored to the needs and challenges of AI technologies. Aimed at masters students, postgraduate trainees, and professionals, this book guides readers through essential components of the data management process, including data collection and preprocessing, ensuring data quality, and establishing effective training and finetuning procedures for AI applications. It also covers AI output validation, compliance measures, and risk mitigation strategies to improve data usability and reliability.