Metadata Representations for Queryable ML Model Zoos
This addresses a problem for ML practitioners and organizations by enabling better management of pre-trained models, though it is incremental as it builds on existing metadata practices.
The paper tackles the lack of standardized and interoperable metadata for ML model zoos, proposing a toolkit to manage and query this metadata to improve model search, reuse, comparison, and composition.
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.