A Cloud-Based Framework for Machine Learning Workloads and Applications

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

A Cloud-Based Framework for Machine Learning Workloads and Applications

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

A Cloud-Based Framework for Machine Learning Workloads and Applications

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/LM2018140" target="_blank" >LM2018140: e-Infrastruktura CZ</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Access

ISSN

2169-3536

e-ISSN

—

Svazek periodika

2020

Číslo periodika v rámci svazku

Vol. 8

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

11

Strana od-do

18681-18692

Kód UT WoS článku

000524755200002

EID výsledku v databázi Scopus

2-s2.0-85079817562