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 meta-data representation and management, proposing a toolkit supported to help practitioners manage and query that metadata.
@article{arxiv.2207.09315,
title = {Metadata Representations for Queryable ML Model Zoos},
author = {Ziyu Li and Rihan Hai and Alessandro Bozzon and Asterios Katsifodimos},
journal= {arXiv preprint arXiv:2207.09315},
year = {2022}
}