yProv4ML: Effortless Provenance Tracking for Machine Learning Systems

TL;DR

yProv4ML is a framework designed to effortlessly capture provenance information during machine learning workflows in a standardized format, enhancing transparency and reproducibility with minimal code changes.

Contribution

It introduces yProv4ML, a novel framework that simplifies provenance tracking in ML systems using PROV-JSON, addressing limitations of existing tools.

Findings

Enables provenance capture with minimal code modifications

Uses PROV-JSON format for standardized lineage data

Improves transparency and reproducibility in ML workflows

Abstract

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.