To turn big data into actionable knowledge, the adoption of machine learning (ML) methods has proven to be one of the de facto approaches.
When elaborating an appropriate ML model for a given task, one typically builds many models and generates several data artifacts.
Given the amount of information associated with the developed models performance, their appropriate selection is often difficult. Therefore, appropriately comparing a set of competitive ML models and choosing one according to an arbitrary set of user metrics require systematic solutions.
In particular, ML model management is a promising research direction for a more systematic and comprehensive approach for machine learning model selection. Therefore, in this paper, we introduce a conceptual model for ML development. Based on this conceptualization, we introduce our vision toward a knowledge-based model management system oriented to model selection.