The proliferation and accessibility of high-performance hardware made cloud computing interesting in the areas of data processing, machine learning and statistics. Users are moving the model training and processing of data to scalable cloud solutions which allow them to execute these processes in a highly parallel manner. This allows them to complete their tasks in less time than on personal computers. But not all tools used by experts have native support for remote execution. In this master’s thesis, we developed a cloud solution for a tool for statistical modeling called Stan. We analyzed and compared cloud solutions for tools similar to Stan. We collected functional requirements and presented the system architecture. Based on the architecture, we developed the platform called Cloudstan, a command-line interface and a library for communicating with the platform written in R.
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