AutoML does not support data augmentation, as it relies on the original dataset for training the model.

AutoML supports data augmentation by manually creating new variations of the existing data, such as adding noise or transforming the data.

AutoML supports data augmentation by using techniques such as random cropping, flipping, and rotation for image data, and synthetic data generation for tabular data to increase the size and diversity of the dataset.

AutoML supports data augmentation by automatically selecting a subset of the original data and applying random transformations to create new variations.