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The recently increased complexity of Machine Learning (ML) methods, led tothe necessity to lighten both the research and industry development processes.ML pipelines have become an essential tool for experts of many domains, datascientists and researchers, allowing them to easily put together several MLmodels to cover the full analytic process starting from raw datasets. Over theyears, several solutions have been proposed to automate the building of MLpipelines, most of them focused on semantic aspects and characteristics of theinput dataset. However, an approach taking into account the new qualityconcerns needed by ML systems (like fairness, interpretability, privacy, etc.)is still missing. In this paper, we first identify, from the literature, keyquality attributes of ML systems. Further, we propose a new engineeringapproach for quality ML pipeline by properly extending the Feature Modelsmeta-model. The presented approach allows to model ML pipelines, their qualityrequirements (on the whole pipeline and on single phases), and qualitycharacteristics of algorithms used to implement each pipeline phase. Finally,we demonstrate the expressiveness of our model considering the classificationproblem.

Modeling Quality and Machine Learning Pipelines through Extended Feature Models