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Authors: Fabian Stieler and Bernhard Bauer

Affiliation: Institute of Computer Science, University of Augsburg, Germany

Keyword(s): Active Learning, Software Engineering for Machine Learning, Machine Learning Operations.

Abstract: As soon as Artificial Intelligence (AI) projects grow from small feasibility studies to mature projects, developers and data scientists face new challenges, such as collaboration with other developers, versioning data, or traceability of model metrics and other resulting artifacts. This paper suggests a data-centric AI project with an Active Learning (AL) loop from a developer perspective and presents ”Git Workflow for AL”: A methodology proposal to guide teams on how to structure a project and solve implementation challenges. We introduce principles for data, code, as well as automation, and present a new branching workflow. The evaluation shows that the proposed method is an enabler for fulfilling established best practices.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Stieler, F. and Bauer, B. (2023). Git Workflow for Active Learning: A Development Methodology Proposal for Data-Centric AI Projects. In Proceedings of the 18th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE; ISBN 978-989-758-647-7; ISSN 2184-4895, SciTePress, pages 202-213. DOI: 10.5220/0011988400003464

@conference{enase23,
author={Fabian Stieler. and Bernhard Bauer.},
title={Git Workflow for Active Learning: A Development Methodology Proposal for Data-Centric AI Projects},
booktitle={Proceedings of the 18th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE},
year={2023},
pages={202-213},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011988400003464},
isbn={978-989-758-647-7},
issn={2184-4895},
}

TY - CONF

JO - Proceedings of the 18th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE
TI - Git Workflow for Active Learning: A Development Methodology Proposal for Data-Centric AI Projects
SN - 978-989-758-647-7
IS - 2184-4895
AU - Stieler, F.
AU - Bauer, B.
PY - 2023
SP - 202
EP - 213
DO - 10.5220/0011988400003464
PB - SciTePress

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