Abstract: Systems, methods, and processor-readable media for elastic allocation of resources for deep learning jobs are described. A machine-learning-as-a-service (MLaaS) of a cloud computing system includes an elastic training module which includes resource allocator for allocating resources to training jobs that optimizes overall estimated time to completion (ETC) for all training jobs received by the system and uses node-based resource allocation. The elastic training module may realize a combination of high resource utilization, short training times, and low queueing delay relative to existing approaches, thereby potentially enabling the realization of higher profits for a cloud computing system which provides MLaaS to users (i.e. customers). An improved user interface is described, enabling users to specify a range of resources to elastically allocate to the user's training job, and/or informing users of training time saved through the use of elastic resource allocation.