MLflow2PROV: Extracting Provenance from Machine Learning Experiments
Open accessAbstract
Supporting iterative and explorative workflows for developing machine learning (ML) models, ML experiment management systems (ML EMSs), such as MLflow, are increasingly used to simplify the structured collection and management of ML artifacts, such as ML models, metadata, and code. However, EMSs typically suffer from limited provenance capabilities. As a consequence, it is hard to analyze provenance information and gain knowledge that can be used to improve both ML models and their development workflows. We propose a W3C-PROV-compliant provenance model capturing ML experiment activities that originate from Git and MLflow usage. Moreover, we present the tool MLflow2PROV that extracts provenance graphs according to our model, enabling querying, analyzing, and further processing of collected provenance information.
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MLOps is viewed from systematic management of ML experiments and assets, leading to version control and experiment recording.
“MLOps is also viewed from the perspective of systematic management of ML experiments and related assets”
From A Multivocal Review of MLOps Practices, Challenges and Open Issues (2026) · cites this paper as [20]
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Cites
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Flagged first, then by citations
Research works printed but not linked: 7
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- Authors
- Schlegel, M., & Sattler, K.-U.
- Affiliations
- Technische Universität Ilmenau (DE)
- Venue
- Proceedings of the Seventh Workshop on Data Management for End-to-End Machine Learning
- Publisher
- ACM
- Published
- 2023-06-18
- Pages
- 1-4
- OpenAlex
- W4380928210
- Cited by
- 17
- References
- 6
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 5 · 2023: 1
- FWCI
- 11.95
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Workshop on Data Management for End-to-End Machine Learning (DEEM) (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
- Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
- Physical Sciences › Computer Science › Information Systems › Research Data Management Practices
- Keywords
- MLflow, W3C PROV, provenance graphs, provenance querying
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