Metadata Representations for Queryable ML Model Zoos

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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.
Original languageEnglish
Number of pages5
Publication statusPublished - 2022
EventICML 2022 Workshop: DataPerf Benchmarking Data for Data-Centric AI - Online event
Duration: 22 Jul 202222 Jul 2022
https://sites.google.com/view/dataperf2022/home?authuser=0

Workshop

WorkshopICML 2022 Workshop: DataPerf Benchmarking Data for Data-Centric AI
Abbreviated titleICML 2022 Workshop
Period22/07/2222/07/22
Internet address

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