Introduction: Machine Learning as a Service (MLaaS) is a capture term for a range of cloud-based platforms that use machine learning tools to produce solutions that help machine learning professionals. Many cloud-based service providers have led the road in recent years to provide I.T. specialists with comparatively cheap and instantly available machine learning solutions to simplify machine learning solutions. As a result, there is a greater need to compare cloud-based services that deliver machine learning solutions. As a result, we chose to evaluate AWS sagemaker and Azure ML cloud services in terms of user experience when loading, training, and serving ML models.                                         

Background: The use of cloud computing has been increased these days, the companies that provide these services have been gradually increased. Although there are many cloud services available on the market, users should always select the more flexible and efficient ones to use. As a result, our research is focused on comparing cloud services in terms of user experience. Assessment approaches and concepts such as Cognitive Walkthrough and Usability heuristics apply to our study as we delve deeper into user interaction and experience. In this case, the user interfaces of Microsoft Azure Machine Learning Studio and Amazon Web Services sagemaker are compared while loading, training, and serving machine learning models.

Objectives: The main objective of this thesis is to compare and evaluate the two cloud services such as AWS sagemaker and Microsoft Azure ML while loading, training, and serving machine learning models to decide which of these two cloud services has the best user interface from the users' perspective using Cognitive Walkthrough.

Methods: Determining the best cloud service in terms of user experience between AWS Sage Maker and Microsoft Azure ML is done using Cognitive Walkthrough by executing selected tasks in both cloud services, and comparison is done using Usability heuristics to reach our research conclusions.

Results: The results originated from the cognitive walkthrough, and comparison with Usability heuristics are presented in graphical formats such as pie charts. The results of cognitive walkthrough are obtained after completion of each task and best cloud service in users’ perspective is obtained.

Conclusions: Finally, we conclude Microsoft Azure machine learning studio is better than AWS sagemaker in terms of user-experience while performing the specified tasks such as loading, training and serving ML models in both the cloud services.