Probabilistic Matrix Factorization for Automated Machine Learning

In embodiments of probabilistic matrix factorization for automated machine learning, a computing system memory maintains different workflows that each include preprocessing steps for a machine learning model, the machine learning model, and one or more parameters for the machine learning model. The computing system memory additionally maintains different data sets, upon which the different workflows can be trained and tested. A matrix is generated from the different workflows and different data sets, where cells of the matrix are populated with performance metrics that each indicate a measure of performance for a workflow applied to a data set. A low-rank decomposition of the matrix with populated performance metrics is then determined. Based on the low-rank decomposition, an optimum workflow for a new data set can be determined. The optimum workflow can be one of the different workflows or a hybrid of at least two of the different workflows.