Automating Data Science: Prospects and Challenges
arXiv:2105.05699 · doi:10.1145/3495256
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.
arXiv comment19 pages, 3 figures. v1 accepted for publication (April 2021) in Communications of the ACM
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