# Metadata Representations for Queryable ML Model Zoos

arXiv: https://arxiv.org/abs/2207.09315 · HTML: https://mlsanity.com/paper/2207.09315/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 15 references that are in the map.
- First posted on arXiv 19 Jul 2022.

## Builds on (most cited first, 6 of 6)

- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [MLPerf Inference Benchmark](https://mlsanity.com/paper/1911.02549/) (2019)
- [MLPerf Training Benchmark](https://mlsanity.com/paper/1910.01500/) (2019)
- [Fathom: Reference Workloads for Modern Deep Learning Methods](https://mlsanity.com/paper/1608.06581/) (2016)
- [ModelHub: Towards Unified Data and Lifecycle Management for Deep Learning](https://mlsanity.com/paper/1611.06224/) (2016)

## Abstract (arXiv)

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.