Metadata Representations for Queryable ML Model Zoos
Z. Li (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Rihan Hai (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Alessandro Bozzon (TU Delft - Industrial Design Engineering)
Asterios Katsifodimos (TU Delft - Electrical Engineering, Mathematics and Computer Science)
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Abstract
Machine learning (ML) practitioners and organizations are building model zoos of pre-trained models, containing metadata describing properties of the ML models and datasets that are useful for reporting, auditing, reproducibility, and interpretability purposes. The metatada is currently not standardised; its expressivity is limited; and there is no interoperable way to store and query it. Consequently, model search, reuse, comparison, and composition are hindered. In this paper, we advocate for standardized ML model metadata representation and management, proposing a toolkit supported to help practitioners manage and query that metadata.