Cornucopia: Tool Support for Selecting Machine Learning Lifecycle Artifact Management Systems

Marius Schlegel, Kai-Uwe Sattler

2022

Abstract

The explorative and iterative nature of developing and operating machine learning (ML) applications leads to a variety of ML artifacts, such as datasets, models, hyperparameters, metrics, software, and configurations. To enable comparability, traceability, and reproducibility of ML artifacts across the ML lifecycle steps and iterations, platforms, frameworks, and tools have been developed to support their collection, storage, and management. Selecting the best-suited ML artifact management systems (AMSs) for a particular use case is often challenging and time-consuming due to the plethora of AMSs, their different focus, and imprecise specifications of features and properties. Based on assessment criteria and their application to a representative selection of more than 60 AMSs, this paper introduces an interactive web tool that enables the convenient and time-efficient exploration and comparison of ML AMSs.

Paper Citation

in Harvard Style

Schlegel M. and Sattler K. (2022). Cornucopia: Tool Support for Selecting Machine Learning Lifecycle Artifact Management Systems. In Proceedings of the 18th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, ISBN 978-989-758-613-2, pages 444-450. DOI: 10.5220/0011591700003318

in Bibtex Style

@conference{webist22,
author={Marius Schlegel and Kai-Uwe Sattler},
title={Cornucopia: Tool Support for Selecting Machine Learning Lifecycle Artifact Management Systems},
booktitle={Proceedings of the 18th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},
year={2022},
pages={444-450},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011591700003318},
isbn={978-989-758-613-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
TI - Cornucopia: Tool Support for Selecting Machine Learning Lifecycle Artifact Management Systems
SN - 978-989-758-613-2
AU - Schlegel M.
AU - Sattler K.
PY - 2022
SP - 444
EP - 450
DO - 10.5220/0011591700003318