Machine learning is a growing area of artificial intelligence that is widely used in our world today. Training machine learning models requires knowledge and computing power. Machine Learning as a Service (MLaaS) tries to solve these issues. By storing the datasets and using virtual computing instances in the cloud, one can create machine learning models without writing a single line of code. When selecting an MLaaS platform to use, the natural question of which one to use arises. This thesis conducts controlled experiments to compare the image classification capabilities of Microsoft Azure ML, Amazon Web Services SageMaker, and Google Cloud Platform Vertex AI. The prediction accuracy, training time, and cost will be measured with three different datasets. Some subjective comments about the user experience while conducting these experiments will also be provided. The results of these experiments will be used to make recommendations as to which MLaaS platform to use depending on which metric is most suitable. This thesis found that Microsoft Azure ML performed best in terms of prediction accuracy, and training cost, across all datasets. Amazon Web Services SageMaker had the shortest time to train but performed the worst in terms of accuracy and had trouble with two of the three datasets. Google Cloud Platform Vertex AI did achieve the second-bestprediction accuracy but was the most expensive platform by far as it had the largest time to train. It did, however, provide the smoothest user experience.Overall, Azure ML would be the platform of choice for image classification tasks after weighing together the results of the experiments as well as the subjective user experience.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:lnu-113829 |
| Date | January 2022 |
| Creators | Berg, Gustav |
| Publisher | Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM) |
| 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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