A conceptual vision toward the management of Machine Learning models

A conceptual vision toward the management of Machine Learning models

Fabio Porto, Adolfo Simões, Carlos Leonardo Souza Cardoso, Rafael Silva Pereira, Hermano Lourenço Souza Lustosa, Klaus Wehmuth, Artur Ziviani, João Guilherme Nobre Rittmeyer, Daniel N. Ramos da Silva, Yania Molina Souto, Douglas Ericson Marcelino de Oliveira, Flavia Delicato, Eduardo Ogasawara, Heleno Campos, Luciana E. G. Vignoli, Rebecca Salles, Paulo de F. Pires

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.

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