Current distributed machine learning prediction models treat deep learning workloads as black boxes since they can not model different characteristics of a neural network.

They have poor out-of-distribution capabilities, meaning that a new deep learning job needs to be trained to be able to have its characteristics predicted, which greatly limits the usability of the prediction models.

Our proposed approach uses most recent neural network property prediction models to leveraging this information to predict never-seen-before machine learning jobs.