Scientific Computing in the Cloud
With the increasing sizes of data sets and computational tasks, cloud-based resources like databases, GPU computation, data processing pipelines, or hosted Jupyter tools have become a critical resource for scientific development. Projects that do not start at massive scales, though, face the challenge of moving from local developer machines or clusters to fully scaling cloud-based resources. This can be quite challenging as this can be a quite different skill set than what is currently developed in scientific educational programs. Thus, researchers must either learn themselves or lobby for administrative support to help design and deploy cloud infrastructure. In this BoF we think it would be helpful for folks who have not worked with cloud resources to have a chance to ask questions and for those who have incorporated cloud resources into their workflows to share advice.