2021/05/12 by Tijl De Bie, Luc De Raedt, José Hernández‐Orallo +4 · 1 voice
Computer Science · Decision Sciences · Engineering · #Automation #Computer science #Data Stream Mining Techniques #Data science #Engineering #Machine Learning and Data Classification #Process (computing) #Scientific Computing and Data Management #cs.DB #cs.LG
paper · pdf · doi:10.1145/3495256
published as Communications of the ACM 65(3) 76-87 (2022) · 19 pages, 3 figures. v1 accepted for publication (April 2021) in Communications of the ACM
arxiv published 2021/05/12 · openalex publication_date 2022/02/23 · openalex created_date 2022/02/24 · arxiv created 2022/02/28 · arxiv updated 2022/03/01 · openalex updated_date 2026/07/22
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.