Abstract: Time-based and accuracy-estimate-based trial selection for selection of subsequent machine-learning models in automated machine learning. In an embodiment, a batch of trials are generated for a plurality of machine-learning algorithms. During execution of the trials, a training time is estimated for at least a portion of the models represented in the batch of trials, and a subset of models are selected for evaluation based, at least in part, on their estimated training times. A graphical user interface is updated to reflect the evaluation results of the subset of models, even before the evaluation results for other models become available.