论文标题
可查询ML模型动物园的元数据表示
Metadata Representations for Queryable ML Model Zoos
论文作者
论文摘要
机器学习（ML）从业人员和组织正在建立预训练模型的模型动物园，其中包含元数据描述ML模型和数据集的属性，这些模型和数据集可用于报告，审计，可重复性和解释性目的。 Metatada目前尚未标准化；它的表现力是有限的；并且没有可互操作的方法来存储和查询它。因此，阻碍了模型搜索，重用，比较和组成。在本文中，我们倡导标准化的ML模型元数据表示和管理，并提出了一个支持从业者管理和查询元数据的工具包。
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.
