A conceptual vision toward the management of Machine Learning models

A conceptual vision toward the management of Machine Learning models

Daniel N. R. da Silva, Yania Souto, Adolfo Simões, Carlos Cardoso, João N. Rittmeyer, Hermano Lustosa, Luciana E. G. Vignoli, Rebecca Salles, Eduardo Ogasawara, Flavia C. Delicato, Paulo de F. Pires, Artur Ziviani and Fabio Porto


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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Dexl Members

Fabio Porto
Adolfo Simões
Carlos Leonardo Souza Cardoso
Rafael Silva Pereira
Hermano Lourenço Souza Lustosa
Klaus Wehmuth
Artur Ziviani (in memoriam)
João Guilherme Nobre Rittmeyer
Daniel N. Ramos da Silva
Yania Molina Souto
Douglas Ericson Marcelino de Oliveira
Flavia Delicato
Flavia Delicato
Eduardo Ogasawara
Eduardo Ogasawara
Heleno Campos
Heleno Campos