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