Metadata Representations for Queryable ML Model Zoos


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.

Workshop on Benchmarking Data for Data-Centric AI (DataPerf) @ICML 2022
Rihan Hai
Rihan Hai
Assistant professor

My research focuses on data integration and related dataset discovery in large-scale data lakes.