Model-Based Data-Centric AI: Bridging the Divide Between Academic Ideals and Industrial Pragmatism

arXiv:2403.01832v1 Announce Type: cross
Abstract: This paper delves into the contrasting roles of data within academic and industrial spheres, highlighting the divergence between Data-Centric AI and Model-Agnostic AI approaches. We argue that while Data-Centric AI focuses on the primacy of high-quality data for model performance, Model-Agnostic AI prioritizes algorithmic flexibility, often at the expense of data quality considerations. This distinction reveals that academic standards for data quality frequently do not meet the rigorous demands of industrial applications, leading to potential …

abstract academic arxiv cs.ai cs.cl data data-centric divergence highlighting industrial model-agnostic paper performance quality quality data roles type