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Título: | A cloud-based framework for machine learning workloads and applications |
Autor: | López García, Álvaro CSIC ORCID ; Marco, Jesús CSIC ORCID ; Antonacci, Marica; Castell, Wolfgang zu; David, Mario; Hardt, Marcus; Lloret Iglesias, Lara CSIC ORCID; Moltó, Germán; Plociennik, Marcin; Tran, Viet; Alic, Andy S.; Caballer, Miguel; Campos, Isabel CSIC ORCID ; Costantini, Alessandro; Dlugolinsky, Stefan; Duma, Doina Cristina; Donvito, Giacinto; Gomes, Jorge; Heredia, Ignacio CSIC ORCID; Ito, Keiichi; Kozlov, Valentin; Nguyen, Giang; Orviz, Pablo CSIC ORCID ; Šustr, Zdenêk; Wolniewicz, Pawel | Palabras clave: | Cloud computing Computers and information processing Deep learning Machine learning |
Fecha de publicación: | 2020 | Editor: | Institute of Electrical and Electronics Engineers | Citación: | IEEE Access 8: 18681-18692 (2020) | Resumen: | 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. | Versión del editor: | https://doi.org/10.1109/ACCESS.2020.2964386 | URI: | http://hdl.handle.net/10261/221872 | DOI: | 10.1109/ACCESS.2020.2964386 | E-ISSN: | 2169-3536 |
Aparece en las colecciones: | (IFCA) Artículos (I3M) Artículos |
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cloudappl.pdf | 1,89 MB | Adobe PDF | Visualizar/Abrir |
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