Abstract: The present disclosure provide techniques for automated machine learning (ML) training based on updating training data based on model performance. A system can determine a sample pattern from input data to be used for training a model. The system determines a training dataset and a test dataset including multiple samples corresponding to the sample pattern. After training a model using the training dataset, the system tests the model using the test dataset. The system determines that the trained model's performance with respect to a sample pattern is unsatisfactory, and retrains the model using an updated training dataset that includes additional samples corresponding to the sample pattern.