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Lopez Garcia, A.* ; Tran, V.* ; Alic, A.S.* ; Caballer, M.* ; Plasencia, I.C.* ; Costantini, A.* ; Dlugolinsky, S.* ; Duma, D.C.* ; Donvito, G.* ; Gomes, J.* ; Heredia Cacha, I.* ; De Lucas, J.M.* ; Ito, K. ; Kozlov, V.Y.* ; Nguyen, G.* ; Orviz Fernandez, P.* ; Sustr, Z.* ; Wolniewicz, P.* ; Antonacci, M.* ; zu Castell, W. ; David, M.* ; Hardt, M.* ; Lloret Iglesias, L.* ; Molto, G.* ; Plociennik, M.*

A cloud-based framework for machine learning workloads and applications.

IEEE Access 8, 18681-18692 (2020)
Publ. Version/Full Text DOI
Open Access Gold
Creative Commons Lizenzvertrag
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.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Cloud Computing ; Computers And Information Processing ; Deep Learning ; Distributed Computing ; Machine Learning ; Serverless Architectures
ISSN (print) / ISBN 2169-3536
e-ISSN 2169-3536
Journal IEEE Access
Quellenangaben Volume: 8, Issue: , Pages: 18681-18692 Article Number: , Supplement: ,
Reviewing status
Institute(s) Helmholtz Pioneer Campus (HPC)
Institute of Computational Biology (ICB)