The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Capturing end-to-end provenance for machine learning pipelines
Abstract
Modern workflows for developing ML pipelines utilize ML artifact management systems (ML AMSs) such as MLflow in addition to traditional version control systems such as Git. ML AMSs collect data, model, metadata and software artifacts used and produced in pipeline development workflows. While ensuring repeatability and reproducibility, the provenance capabilities are still rudimentary, mainly due to incomplete traces, coarse granularity, and limited query capabilities. In this paper, we introduce a comprehensive PROV-compliant provenance model that captures end-to-end provenance traces of ML pipelines, their artifacts, and their relationships based on MLflow and Git activities. Moreover, we present the tool MLflow2PROV for continuously extracting provenance graphs according to our model, enabling querying, analyzing, and processing of the collected provenance information.
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Cites
Advances and Open Problems in Federated Learning
Kairouz, P., & McMahan, H. B. (2021) · Foundations and Trends in Machine Learning · doi:10.1561/2200000083 · Cited by 7,772
Agile Software Development, Principles, Patterns, and Practices
Martin, R. (2002) · Cited by 1,466
Modeling wine preferences by data mining from physicochemical properties
Cortez, P., Cerdeira, A., Almeida, F., Matos, T., & Reis, J. (2009) · Decision Support Systems · doi:10.1016/j.dss.2009.05.016 · Cited by 1,273
OpenML: networked science in machine learning
Vanschoren, J., van Rijn, J. N., Bischl, B., & Torgo, L. (2014) · ACM SIGKDD Explorations Newsletter · doi:10.1145/2641190.2641198 · Cited by 1,141
Software Engineering for Machine Learning: A Case Study
Amershi, S., Begel, A., Bird, C., DeLine, R., Gall, H., Kamar, E., Nagappan, N., Nushi, B., & Zimmermann, T. (2019) · 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) · doi:10.1109/icse-seip.2019.00042 · Cited by 1,135
Packaging research artefacts with RO-Crate
Soiland‐Reyes, S., Sefton, P., Crosas, M., Castro, L. J., Coppens, F., Fernández, J. M., Garijo, D., Grüning, B. A., Rosa, M. L., Leo, S., et al. (2022) · Data Science · doi:10.3233/ds-210053 · Cited by 280
SKOS Simple Knowledge Organization System Primer
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PROV-Overview. An Overview of the PROV Family of Documents
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Developments in MLflow: A System to Accelerate the Machine Learning Lifecycle
Chen, A., Chow, A., Davidson, A., DCunha, A., Ghodsi, A., Hong, S. A., Konwinski, A., Mewald, C., Murching, S., Nykodym, T., et al. (2020) · Proceedings of the Fourth International Workshop on Data Management for End-to-End Machine Learning · doi:10.1145/3399579.3399867 · Cited by 112
Management of Machine Learning Lifecycle Artifacts: A Survey
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Self-citation
Research works printed but not linked: 5
- Zaharia, 2018, Accelerating the Machine Learning Lifecycle with MLflow, IEEE Data Eng. Bull., 41, 39
- Huynh, 2014, ProvStore: A Public Provenance Repository, vol. 8628, 275
- Schlegel, 2023, Extracting Provenance of Machine Learning Experiment Pipeline Artifacts, vol. 13985, 238
- Chapman, 2022, DPDS: Assisting Data Science with Data Provenance, PVLDB, 15, 3614
- S. Schelter, J.-H. Böse, J. Kirschnick, T. Klein, S. Seufert, Automatically Tracking Metadata and Provenance of Machine Learning Experiments, in: MLSys@NIPS ’17, 2017, pp. 1–8.
Metadata
- Authors
- Schlegel, M., & Sattler, K.-U.
- Venue
- Information Systems
- Publisher
- Elsevier BV
- Published
- 2025-07
- Volume
- 132
- Pages
- 102495
- ISSN
- 0306-4379
- Cited by
- 32
- References
- 21
- Language
- en
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- creativecommons.org/licenses/by/4.0/ (vor)
- Funders
- Thüringer Ministerium für Wirtschaft, Wissenschaft und Digitale Gesellschaft (5575/10-3)
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- Schlegel, M., & Sattler, K.-U. (2025). Capturing end-to-end provenance for machine learning pipelines. Information Systems. https://doi.org/10.1016/j.is.2024.102495
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