# MLDev: Data Science Experiment Automation and Reproducibility Software

arXiv: https://arxiv.org/abs/2107.12322 · HTML: https://mlsanity.com/paper/2107.12322/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 31 references that are in the map.
- First posted on arXiv 26 Jul 2021.

## Builds on (most cited first, 5 of 5)

- [Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)](https://mlsanity.com/paper/2003.12206/) (2020)
- [Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs](https://mlsanity.com/paper/1906.01195/) (2019)
- [Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis](https://mlsanity.com/paper/1805.04276/) (2018)
- [A large-scale study on research code quality and execution](https://mlsanity.com/paper/2103.12793/) (2021)
- [Analysis of hidden feedback loops in continuous machine learning systems](https://mlsanity.com/paper/2101.05673/) (2021)

## Built on by (most cited first, 1 of 1)

- [A Mathematical Model of the Hidden Feedback Loop Effect in Machine Learning Systems](https://mlsanity.com/paper/2405.02726/) (2024)

## Abstract (arXiv)

In this paper we explore the challenges of automating experiments in data science. We propose an extensible experiment model as a foundation for integration of different open source tools for running research experiments. We implement our approach in a prototype open source MLDev software package and evaluate it in a series of experiments yielding promising results. Comparison with other state-of-the-art tools signifies novelty of our approach.