MLDev: Data Science Experiment Automation and Reproducibility Software
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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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 5 of its 31 references that are in the map. First posted on arXiv 26 Jul 2021.
Builds on
5 of 31 references.
References · 5
- 2020Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)cited by 318
- 2019Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphscited by 125
- 2018Leveraging Grammar and Reinforcement Learning for Neural Program Synthesiscited by 83
- 2021A large-scale study on research code quality and executioncited by 22
- 2021Analysis of hidden feedback loops in continuous machine learning systemscited by 2
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Citing papers per year: 2024: 1.
1 paper.