A runtime execution environment for machine-learning laboratory work

Abstract

Machine-learning requires resources that are usually not available on students private computers/laptops. Instead, students will often need to upload their code to on- or off-campus facilities. This process may be quite challenging for novice students. To mitigate this, we have developed a system for remote execution of machine-learning algorithms on public or private cloud-like environments.