Automating Data Science: Prospects and Challenges [article]

Tijl De Bie, Luc De Raedt, José Hernández-Orallo, Holger H. Hoos, Padhraic Smyth, Christopher K. I. Williams
2021 arXiv   pre-print
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
more » ... er to automate, not only because of technological challenges, but because open-ended and context-dependent tasks require human interaction.
arXiv:2105.05699v1 fatcat:zmfdufmnzrc47hanwx5t33efwu