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Metrics for Evaluating Machine Learning Cloud Services
Jönköping University, School of Engineering, JTH, Computer Science and Informatics.
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Machine Learning (ML) is nowadays being offered as a service by several cloud providers. Consumers require metrics to be able to evaluate and compare between multiple ML cloud services. There aren’t many established metrics that can be used specifically for these types of services. In this paper, the Goal-QuestionMetric paradigm is used to define a set of metrics applicable for ML cloud services. The metrics are created based on goals expressed by professionals who use or are interested in using these services. At the end, a questionnaire is used to evaluate the metrics based on two criteria: relevance and ease of use.

Place, publisher, year, edition, pages
2017. , p. 99
Keyword [en]
Metrics, Machine Learning, Cloud services, MLaaS, GQM, Evaluation
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:hj:diva-37882ISRN: JU-JTH-PRU-2-20170085OAI: oai:DiVA.org:hj-37882DiVA, id: diva2:1158258
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Available from: 2017-12-05 Created: 2017-11-19 Last updated: 2017-12-05Bibliographically approved

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Metrics for Evaluating Machine Learning Cloud Services(2741 kB)44 downloads
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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • de-DE
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Output format
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