Abstract: Methods and systems are described herein for improving the performance of machine learning models using heterogeneous datasets via dynamic loss functions. For example, the system may determine whether a dataset to be used to train a machine learning model comprises a heterogeneous dataset characteristic. In response to the dataset comprising the heterogeneous dataset characteristic, the system selects a dynamic loss function, wherein the loss function comprises a hyperparameter that is based on segments of a segmented dataset distribution. The system may then segment the dataset and generate a set of dynamic hyperparameter values, where each hyperparameter value corresponds to a respective data segment of the segmented dataset. The system may then provide the dataset to a machine learning model to train the machine learning model using the dynamic loss function.