Paper Info
Title
A Cloud-Based Framework For Machine Learning Workloads And Applications
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.
Year
DOI
Venue
2020
10.1109/ACCESS.2020.2964386
IEEE ACCESS
Keywords
DocType
Volume
Cloud computing, computers and information processing, deep learning, distributed computing, machine learning, serverless architectures
Journal
8
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
25
Name
Order
Citations
PageRank
Álvaro López García1497.19
Viet D. Tran26515.84
Andy S. Alic331.46
Miguel Caballer417216.90
Isabel Campos Plasencia5287.00
Alessandro Costantini600.34
Stefan Dlugolinsky7549.32
Doina Cristina Duma800.34
Giacinto Donvito9606.04
Jorge Gomes1000.34
Ignacio Heredia Cacha1100.34
Jesus Marco De Lucas1200.34
Keiichi Ito1300.34
Valentin Y. Kozlov1400.34
Giang T. Nguyen156716.69
Pablo Orviz Fernández16173.71
Zdenek Sustr1712.09
Pawel Wolniewicz18828.65
Marica Antonacci1900.34
Wolfgang Zu Castell2000.34
M. David21234.28
Marcus Hardt2200.34
Lara Lloret Iglesias2300.34
Germen Molto2400.34
Marcin Plociennik2500.34