Wen-Chieh-Lee Overview
In the traditional model-centric AI lifecycle, the focus is on finding better models to enhance performance, with data largely unchanged. This approach overlooks data quality issues like missing values, incorrect labels, and anomalies. Data-centric AI shifts the emphasis from models to systematic data engineering to build AI systems.
