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  • 标题:Empirical Insights on Cloud Services for Machine Learning Applications
  • 本地全文:下载
  • 作者:Adrian MICU ; Marius GERU ; Angela-Eliza MICU
  • 期刊名称:Annals of Dunărea de Jos University. Fascicle I : Economics and Applied Informatics
  • 印刷版ISSN:1584-0409
  • 出版年度:2020
  • 卷号:26
  • 期号:2
  • 页码:85-90
  • 语种:English
  • 出版社:Dunarea de Jos University of Galati
  • 摘要:As the volume of data increases, becomes more complex and valuable, people's limited capabilities present real challenges in deciphering and interpreting an increasingly unpredictable economic environment. In essence, Machine Learning is the artifact of artificial intelligence generated and shared mainly by the technological environment, where almost any information can be documented, measured and stored digitally, thus becoming data that can be processed to generate actionable information reusable in multiple spheres. of activity. The aim of this research is a comparative analysis of the main cloud services available for Machine Learning algorithms. The research results offer a dynamic vision to the researchers involved in the FutureWeb project, who are looking for the most efficient cloud platforms for the services offered by the AI Media platform.
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