A Cloud-Based Framework for Machine Learning Workloads and Applications
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F63839172%3A_____%2F20%3A10133284" target="_blank" >RIV/63839172:_____/20:10133284 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/8950411" target="_blank" >https://ieeexplore.ieee.org/document/8950411</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ACCESS.2020.2964386" target="_blank" >10.1109/ACCESS.2020.2964386</a>
Alternative languages
Result language
angličtina
Original language name
A Cloud-Based Framework for Machine Learning Workloads and Applications
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/LM2018140" target="_blank" >LM2018140: e-Infrastructure CZ</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Access
ISSN
2169-3536
e-ISSN
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Volume of the periodical
2020
Issue of the periodical within the volume
Vol. 8
Country of publishing house
US - UNITED STATES
Number of pages
11
Pages from-to
18681-18692
UT code for WoS article
000524755200002
EID of the result in the Scopus database
2-s2.0-85079817562