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.
Original language | English |
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Number of pages | 5 |
Publication status | Published - 2022 |
Event | ICML 2022 Workshop: DataPerf Benchmarking Data for Data-Centric AI - Online event Duration: 22 Jul 2022 → 22 Jul 2022 https://sites.google.com/view/dataperf2022/home?authuser=0 |
Workshop
Workshop | ICML 2022 Workshop: DataPerf Benchmarking Data for Data-Centric AI |
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Abbreviated title | ICML 2022 Workshop |
Period | 22/07/22 → 22/07/22 |
Internet address |