A knowledge-driven AutoML architecture

Automated machine learning (AutoML) gathered a significant amount of attention in recent years as a way of automating some of the typical workflows in machine learning (ML) and data science more broadly. For a comprehensive and systematic view on the subject, there is an already growing number of survey works that cover the state-of-the-art Hutter et al. (2019); Yao et al. (2018); Elshawi et al. (2019); Zöller and Huber (2021); Truong et al. (2019); He et al. (2021); Hospedales et al. (2020); Vanschoren (2018Santu"); Karmaker Santu"Santu". Currently, it is becoming apparent that the size of the potential problem space, required solution sophistication, transparency and legal constraints Roscher et al. (2020); Drozdal et al. (2020); Rudin et al. (2021); Veale and Borgesius (2021); Smuha et al. (2021) render AutoML a problem extremely difficult to define and solve either holistically or agnostically.