Data Management for the AI Era: Ensuring Reliability and Accountability
IFIP WG 2.6, Invited Selection
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Beschreibung
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 an up-to-date presentation of the data management process created by AI technologies this book explores data management practices tailored to the needs and challenges of AI technologies this book guides readers through essential components of the data management process
Autor*in
Paolo Ceravolo
Themen in »Data Management for the AI Era: Ensuring Reliability and Accountability«
Machine Learning Model Monitoring Concept Drift Detection Explainability / Model Interpretability Audit Trails / Logging Data Integration Performance Evaluation Predictive Analytics Data Quality Management Continuous Validation Human-Computer Interaction (HCI) Algorithmic Fairness Data Management Systems Database Integration Information Systems Applications