Towards a Software Product Line for Machine Learning Workflows: Focus on Supporting Evolution

Cécile Camillieri (1, 2) , Luca Parisi (1, 2) , Mireille Blay-Fornarino (1, 2) , Frédéric Precioso (1, 2) , Michel Riveill (1, 2) , Joël Cancela-Vaz (1, 2)

1 Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
2 I3S - Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis

The purpose of the ROCKFlows project is to lay the foundations of a Software Product Line (SPL) that helps the construction of machine learning workflows. Based on her data and objectives, the end user, who is not necessarily an expert, should be presented with workflows that address her needs in the " best possible way ". To make such a platform durable, data scientists should be able to integrate new algorithms that can be compared to existing ones in the system, thus allowing to grow the space of available solutions. While comparing the algorithms is challenging in itself, Machine Learning, as a constantly evolving, extremely complex and broad domain, requires the definition of specific and flexible evolution mechanisms. In this paper, we focus on mechanisms based on meta-modelling techniques to automatically enrich a SPL while ensuring its consistency.

Domaines: Interface homme-machine [cs.HC]; Génie logiciel [cs.SE]; Intelligence artificielle [cs.AI]; Apprentissage [cs.LG]

Cécile Camillieri, Luca Parisi, Mireille Blay-Fornarino, Frédéric Precioso, Michel Riveill, et al.. Towards a Software Product Line for Machine Learning Workflows: Focus on Supporting Evolution. 10th Workshop on Models and Evolution co-located with ACM/IEEE 19th International Conference on Model Driven Engineering Languages and Systems (MODELS 2016), Oct 2016, Saint Malo, France. ⟨hal-01484050⟩