Metadata Representations for Queryable ML Model Zoos

Conference paper (2022)

Authors

Z. Li Web Information Systems - EEMCS
R. Hai Web Information Systems - EEMCS
A. Bozzon Human-Centred Artificial Intelligence - IDE
A Katsifodimos Web Information Systems - EEMCS
Research Group
Web Information Systems (EEMCS) (TU Delft)
Copyright: © 2022 Z. Li, R. Hai, A. Bozzon, A Katsifodimos
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Published Date

2022

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Software Technology

Research Group

Web Information Systems

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.

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