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(57) ABSTRACT A proxy-based automatic non-iterative machine learning (PANI-ML) pipeline is described, which predicts machine learning model configuration performance and outputs an automatically-configured machine learning model for a tar get training dataset. Techniques described herein use one or more proxy models—which implement a variety of machine learning algorithms and are pre-configured with tuned hyperparameters to estimate relative performance of machine learning model configuration parameters at various stages of the PANI-ML pipeline. The PANI-ML pipeline implements a radically new approach of rapidly narrowing the search space for machine learning model configuration parameters by performing algorithm selection followed by algorithm-specific adaptive data reduction (ie, row-and/or feature-wise dataset sampling), and then hyperparameter tuning. Furthermore, because of the …