학술논문
A Cloud-Based Framework for Machine Learning Workloads and Applications
Document Type
Periodical
Author
Lopez Garcia, A.; De Lucas, J.M.; Antonacci, M.; Zu Castell, W.; David, M.; Hardt, M.; Lloret Iglesias, L.; Molto, G.; Plociennik, M.; Tran, V.; Alic, A.S.; Caballer, M.; Plasencia, I.C.; Costantini, A.; Dlugolinsky, S.; Duma, D.C.; Donvito, G.; Gomes, J.; Heredia Cacha, I.; Ito, K.; Kozlov, V.Y.; Nguyen, G.; Orviz Fernandez, P.; Sustr, Z.; Wolniewicz, P.
Source
IEEE Access Access, IEEE. 8:18681-18692 2020
Subject
Language
ISSN
2169-3536
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.