Dataset Curation for High-Quality AI Systems
Dataset curation determines whether an AI model trains on useful, representative, and bias-resistant data. Unlike dataset preparation, which focuses on formatting and preprocessing, dataset curation focuses on deciding what should and should not belong in the dataset. This article explores how to systematically filter noise, identify outliers, balance classes, refine diversity, and maintain long-term dataset health across industries. By applying structured curation frameworks, AI teams can significantly improve reliability, fairness, and real-world model behavior.
