Techniques are provided for predicting a time-to-finish of at least one workflow in a shared computing environment using a deep neural network with a biangular activation function. An exemplary method comprises: obtaining a specification of an executing workflow of multiple concurrent workflows in a shared computing environment, wherein the specification comprises states of past executions of the executing workflow; obtaining a trained deep neural network, wherein the trained deep neural network is trained to predict one or more future states of the executing workflow using the states of past executions and wherein the trained deep neural network employs a biangular activation function comprising multiple parameters that define a position and a slope associated with two angles of the biangular activation function for a range of input values; and estimating, using the at least one trained deep neural network, a time-to-finish of the executing workflow of the multiple concurrent workflows.