# yProv4ML: Effortless Provenance Tracking for Machine Learning Systems

arXiv: https://arxiv.org/abs/2507.01078 · HTML: https://mlsanity.com/paper/2507.01078/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 1 Jul 2025.

## Builds on (most cited first, 4 of 4)

- [Packaging research artefacts with RO-Crate](https://mlsanity.com/paper/2108.06503/) (2021)
- [Reproducibility in Machine Learning-Driven Research](https://mlsanity.com/paper/2307.10320/) (2023)
- [Towards Lightweight Data Integration using Multi-workflow Provenance and Data Observability](https://mlsanity.com/paper/2308.09004/) (2023)
- [Supporting Better Insights of Data Science Pipelines with Fine-grained Provenance](https://mlsanity.com/paper/2310.18079/) (2023)

## Built on by (most cited first, 2 of 2)

- [Verifying Machine Learning Interpretability Requirements through Provenance](https://mlsanity.com/paper/2604.21599/) (2026)
- [Workflow Cards: Structured Summaries of Workflow Executions Using Provenance Data](https://mlsanity.com/paper/2608.11022/) (2026)

## Abstract (arXiv)

The rapid growth of interest in large language models (LLMs) reflects their potential for flexibility and generalization, and attracted the attention of a diverse range of researchers. However, the advent of these techniques has also brought to light the lack of transparency and rigor with which development is pursued. In particular, the inability to determine the number of epochs and other hyperparameters in advance presents challenges in identifying the best model. To address this challenge, machine learning frameworks such as MLFlow can automate the collection of this type of information. However, these tools capture data using proprietary formats and pose little attention to lineage. This paper proposes yProv4ML, a framework to capture provenance information generated during machine learning processes in PROV-JSON format, with minimal code modifications.