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.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:hj-37882 |
| Date | January 2017 |
| Creators | Tataru, Augustin |
| Publisher | Tekniska Högskolan, Högskolan i Jönköping, JTH, Datateknik och informatik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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