Automating Data Science: Prospects and Challenges
Databases
2022-03-01 v2 Machine Learning
Abstract
Given the complexity of typical data science projects and the associated demand for human expertise, automation has the potential to transform the data science process. Key insights: * Automation in data science aims to facilitate and transform the work of data scientists, not to replace them. * Important parts of data science are already being automated, especially in the modeling stages, where techniques such as automated machine learning (AutoML) are gaining traction. * Other aspects are harder to automate, not only because of technological challenges, but because open-ended and context-dependent tasks require human interaction.
Cite
@article{arxiv.2105.05699,
title = {Automating Data Science: Prospects and Challenges},
author = {Tijl De Bie and Luc De Raedt and José Hernández-Orallo and Holger H. Hoos and Padhraic Smyth and Christopher K. I. Williams},
journal= {arXiv preprint arXiv:2105.05699},
year = {2022}
}
Comments
19 pages, 3 figures. v1 accepted for publication (April 2021) in Communications of the ACM