IEEE Access (Jan 2020)

A Cloud-Based Framework for Machine Learning Workloads and Applications

  • 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,
  • Pawel Wolniewicz

DOI
https://doi.org/10.1109/ACCESS.2020.2964386
Journal volume & issue
Vol. 8
pp. 18681 – 18692

Abstract

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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.

Keywords