MLDev: Data Science Experiment Automation and Reproducibility Software

Abstract

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.

11 pages, 2 figures

References in corpus (2)

• Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)
• Analysis of hidden feedback loops in continuous machine learning systems