# Machine Learning Pipelines: Provenance, Reproducibility and FAIR Data Principles

arXiv: https://arxiv.org/abs/2006.12117 · HTML: https://mlsanity.com/paper/2006.12117/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jun 2020.

## Built on by (most cited first, 15 of 15)

- [Deep Learning for Computational Cytology: A Survey](https://mlsanity.com/paper/2202.05126/) (2022)
- [FAIR for AI: An interdisciplinary and international community building perspective](https://mlsanity.com/paper/2210.08973/) (2022)
- [Reproducibility of Machine Learning: Terminology, Recommendations and Open Issues](https://mlsanity.com/paper/2302.12691/) (2023)
- [Preventing Machine Learning Poisoning Attacks Using Authentication and Provenance](https://mlsanity.com/paper/2105.10051/) (2021)
- [Data Science and Machine Learning in Education](https://mlsanity.com/paper/2207.09060/) (2022)
- [Unsupervised Machine Learning for Scientific Discovery: Workflow and Best Practices](https://mlsanity.com/paper/2506.04553/) (2025)
- [Towards Launching AI Algorithms for Cellular Pathology into Clinical & Pharmaceutical Orbits](https://mlsanity.com/paper/2112.09496/) (2021)
- [Untangling Knots: Leveraging LLM for Error Resolution in Computational Notebooks](https://mlsanity.com/paper/2405.01559/) (2024)
- [An Open API Architecture to Discover the Trustworthy Explanation of Cloud AI Services](https://mlsanity.com/paper/2411.03376/) (2024)
- [EdnaML: A Declarative API and Framework for Reproducible Deep Learning](https://mlsanity.com/paper/2211.06783/) (2022)
- [Online learning techniques for prediction of temporal tabular datasets with regime changes](https://mlsanity.com/paper/2301.00790/) (2022)
- [TraceFL: Interpretability-Driven Debugging in Federated Learning via Neuron Provenance](https://mlsanity.com/paper/2312.13632/) (2023)
- [Verifying Machine Learning Interpretability Requirements through Provenance](https://mlsanity.com/paper/2604.21599/) (2026)
- [Helix 1.0: An Open-Source Framework for Reproducible and Interpretable Machine Learning on Tabular Scientific Data](https://mlsanity.com/paper/2507.17791/) (2025)
- [Embedding Provenance in Computer Vision Datasets with JSON-LD](https://mlsanity.com/paper/2603.27348/) (2026)

## Abstract (arXiv)

Machine learning (ML) is an increasingly important scientific tool supporting decision making and knowledge generation in numerous fields. With this, it also becomes more and more important that the results of ML experiments are reproducible. Unfortunately, that often is not the case. Rather, ML, similar to many other disciplines, faces a reproducibility crisis. In this paper, we describe our goals and initial steps in supporting the end-to-end reproducibility of ML pipelines. We investigate which factors beyond the availability of source code and datasets influence reproducibility of ML experiments. We propose ways to apply FAIR data practices to ML workflows. We present our preliminary results on the role of our tool, ProvBook, in capturing and comparing provenance of ML experiments and their reproducibility using Jupyter Notebooks.