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A Cloud-Based Framework for Machine Learning Workloads and Applications

Lopez Garcia, Alvaro; Tran, Viet; Alic, Andy S.; Caballer, Miguel; Plasencia, Isabel Campos; Costantini, Alessandro; Dlugolinsky, Stefan; Duma, Doina Cristina; Donvito, Giacinto; Gomes, Jorge; Heredia Cacha, Ignacio; De Lucas, Jesus Marco; Ito, Keiichi; Kozlov, Valentin Y.; Nguyen, Giang; Orviz Fernandez, Pablo; Sustr, Zdenek; Wolniewicz, Pawel; Antonacci, Marica; Zu Castell, Wolfgang; ... mehr

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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Verlagsausgabe §
DOI: 10.5445/IR/1000117464
Veröffentlicht am 02.03.2020
DOI: 10.1109/ACCESS.2020.2964386
Zitationen: 2
Web of Science
Zitationen: 1
Cover der Publikation
Zugehörige Institution(en) am KIT Steinbuch Centre for Computing (SCC)
Publikationstyp Zeitschriftenaufsatz
Publikationsdatum 05.01.2020
Sprache Englisch
Identifikator ISSN: 2169-3536
KITopen-ID: 1000117464
HGF-Programm 46.12.02 (POF III, LK 01) Data Activities
Erschienen in IEEE access
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Band 8
Seiten 18681–18692
Projektinformation DEEP-HybridDataCloud (EU, H2020, 777435)
Nachgewiesen in Scopus
Web of Science
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