A Cloud-Based Framework for Machine Learning Workloads and Applications
Álvaro López García, J. Marco, Marica Antonacci, Wolfgang zu Castell, M. David, Marcus Hardt, L. Lloret Iglesias, Germán Moltó, Marcin Płóciennik, Viet Tran, Andy S. Alic, Miguel Caballer, Isabel Campos, Alessandro Costantini, Štefan Dlugolinský, M. Dūma, Giacinto Donvito, Jorge Gomes, Ignacio Heredia, Keiichi Ito, В. Козлов, Giang Nguyen, Pablo Orviz, Zdeněk Šustr, Paweł Wolniewicz
Abstract
In this paper we propose a distributed architecture to provide machine learning practitioners with a set of tools and cloud services that cover the whole machine learning development cycle: ranging from the models creation, training, validation and testing to the models serving as a service, sharing and publication. In such respect, the DEEP-Hybrid-DataCloud framework allows transparent access to existing e-Infrastructures, effectively exploiting distributed resources for the most compute-intensive tasks coming from the machine learning development cycle. Moreover, it provides scientists with a set of Cloud-oriented services to make their models publicly available, by adopting a serverless architecture and a DevOps approach, allowing an easy share, publish and deploy of the developed models.