Robust ML models are driven by diverse training data. Here's a simple yet highly effective technique that can help you create a diverse dataset and increase model performance. One way to increase data diversity is using data augmentation. The idea is to create new samples by transforming the available samples. This can prevent overfitting, improve performance, and build robust models.