Model drift in AutoML refers to the change in the distribution of input data or the relationships between features and target variables over time, which can degrade the performance of deployed models. AutoML tools monitor for model drift by continuously evaluating the model's performance and detecting data drift through statistical measures. Techniques to address model drift include model retraining with updated data, adjusting hyperparameters, and incorporating new features. These techniques help maintain the performance of deployed models by adapting to changing data patterns and ensuring the model remains relevant and accurate.