Pregled bibliografske jedinice broj: 1038858
Interoperability of Machine Learning Services: A Use Case
Interoperability of Machine Learning Services: A Use Case // Books of Proceedings of the 49thInternational Scientific Conference on Economic and Social DevelopmentDevelopment –"Building Resilient Society" / Dukic, Darko ; Studzieniecki, Tomasz ; Grzinic, Jasmina (ur.).
Zagreb: VADEA, 2019. str. 106-113 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 1038858 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Interoperability of Machine Learning Services: A Use Case
Autori
Andročec, Darko ; Tikvica, Andrea
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Books of Proceedings of the 49thInternational Scientific Conference on Economic and Social DevelopmentDevelopment –"Building Resilient Society"
/ Dukic, Darko ; Studzieniecki, Tomasz ; Grzinic, Jasmina - Zagreb : VADEA, 2019, 106-113
Skup
49th International Scientific Conference on Economic and Social Development: Building Resilient Society
Mjesto i datum
Zagreb, Hrvatska, 13.12.2019. - 14.12.2019
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
cloud ; interoperability ; machine learning ; services
Sažetak
Machine learning has become very popular because the supporting infrastructure is now available for reasonable price and most companies have big data sources for solving problems in a high-dimensional space. There are many business use cases that show successful application of machine learning methods. However, machine learning methods are complex, so many cloud providers (e.g. Google, Amazon, Microsoft) have recently started to offer different machine learning services. SMEs often want to use multiple machine learning services of different cloud providers. In this work, we present current state-of-the-art of machine learning services interoperability research. Additionally, a real use case is implemented to show how SMEs can use two machine learning services of two different cloud providers in one application. Concretely, a web application has been developed by implementing machine learning services of two different cloud service providers. The following services are used within the practical example: text detection with the support of the Google Cloud Platform and text translation services with the support of Microsoft Azure. The idea of the practical example is to show the interoperability of the mentioned platforms.
Izvorni jezik
Engleski
Znanstvena područja
Informacijske i komunikacijske znanosti
POVEZANOST RADA
Ustanove:
Fakultet organizacije i informatike, Varaždin
Profili:
Darko Andročec
(autor)
Citiraj ovu publikaciju:
Časopis indeksira:
- HeinOnline