# Provenance Data in the Machine Learning Lifecycle in Computational Science and Engineering

arXiv: https://arxiv.org/abs/1910.04223 · HTML: https://mlsanity.com/paper/1910.04223/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2019.

## Built on by (most cited first, 13 of 13)

- [FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging](https://mlsanity.com/paper/2109.09658/) (2021)
- [Management of Machine Learning Lifecycle Artifacts: A Survey](https://mlsanity.com/paper/2210.11831/) (2022)
- [Preventing Machine Learning Poisoning Attacks Using Authentication and Provenance](https://mlsanity.com/paper/2105.10051/) (2021)
- [Provenance Tracking in Large-Scale Machine Learning Systems](https://mlsanity.com/paper/2507.01075/) (2025)
- [Know Your Model (KYM): Increasing Trust in AI and Machine Learning](https://mlsanity.com/paper/2106.11036/) (2021)
- [Optimization's Neglected Normative Commitments](https://mlsanity.com/paper/2305.17465/) (2023)
- [Data Origin Inference in Machine Learning](https://mlsanity.com/paper/2211.13416/) (2022)
- [EdnaML: A Declarative API and Framework for Reproducible Deep Learning](https://mlsanity.com/paper/2211.06783/) (2022)
- [TraceFL: Interpretability-Driven Debugging in Federated Learning via Neuron Provenance](https://mlsanity.com/paper/2312.13632/) (2023)
- [Private Training Set Inspection in MLaaS](https://mlsanity.com/paper/2305.09058/) (2023)
- [From Privacy to Trust in the Agentic Era: A Taxonomy of Challenges in Trustworthy Federated Learning Through the Lens of Trust Report 2.0](https://mlsanity.com/paper/2507.15796/) (2025)
- [ExeKGLib: A Platform for Machine Learning Analytics based on Knowledge Graphs](https://mlsanity.com/paper/2508.00394/) (2025)
- [Enhancing Transparency and Traceability in Healthcare AI: The AI Product Passport](https://mlsanity.com/paper/2512.13702/) (2025)

## Abstract (arXiv)

Machine Learning (ML) has become essential in several industries. In Computational Science and Engineering (CSE), the complexity of the ML lifecycle comes from the large variety of data, scientists' expertise, tools, and workflows. If data are not tracked properly during the lifecycle, it becomes unfeasible to recreate a ML model from scratch or to explain to stakeholders how it was created. The main limitation of provenance tracking solutions is that they cannot cope with provenance capture and integration of domain and ML data processed in the multiple workflows in the lifecycle while keeping the provenance capture overhead low. To handle this problem, in this paper we contribute with a detailed characterization of provenance data in the ML lifecycle in CSE; a new provenance data representation, called PROV-ML, built on top of W3C PROV and ML Schema; and extensions to a system that tracks provenance from multiple workflows to address the characteristics of ML and CSE, and to allow for provenance queries with a standard vocabulary. We show a practical use in a real case in the Oil and Gas industry, along with its evaluation using 48 GPUs in parallel.