1. A Cloud-Based Framework for Machine Learning Workloads and Applications
- Author
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Alvaro Lopez Garcia, Jesus Marco De Lucas, Marica Antonacci, Wolfgang Zu Castell, Mario David, Marcus Hardt, Lara Lloret Iglesias, Germen Molto, Marcin Plociennik, Viet Tran, Andy S. Alic, Miguel Caballer, Isabel Campos Plasencia, Alessandro Costantini, Stefan Dlugolinsky, Doina Cristina Duma, Giacinto Donvito, Jorge Gomes, Ignacio Heredia Cacha, Keiichi Ito, Valentin Y. Kozlov, Giang Nguyen, Pablo Orviz Fernandez, Zdenek Sustr, and Pawel Wolniewicz
- Subjects
Cloud computing ,computers and information processing ,deep learning ,distributed computing ,machine learning ,serverless architectures ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - 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.
- Published
- 2020
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