  Two key factors dominate the development of effective production grade
machine learning models. First, it requires a local software implementation and
iteration process. Second, it requires distributed infrastructure to
efficiently conduct training and hyperparameter optimization. While modern
machine learning frameworks are very effective at the former, practitioners are
often left building ad hoc frameworks for the latter. We present SigOpt
Orchestrate, a library for such simultaneous training in a cloud environment.
We describe the motivating factors and resulting design of this library,
feedback from initial testing, and future goals.
