Which critical activity is undertaken to mitigate the effects of model drift?

Model retraining is a critical activity undertaken to mitigate the effects of model drift because it involves updating the machine learning model with new data to ensure that its predictions remain accurate and relevant over time. As the underlying data distribution changes—due to factors like changes in user behavior, seasonal variations, or other dynamic environments—the performance of a pre-existing model can decline. Retraining allows the model to adapt to these shifts by learning from the most current data, thereby maintaining its effectiveness in making predictions.

Regularly retraining models helps ensure that they continue to perform well in real-world applications where conditions can change rapidly. This ongoing process can involve using incremental learning techniques or complete retraining with a full dataset that includes both old and new examples, depending on the specific requirements of the situation.

In contrast, the other options do not specifically address the issue of model drift. While batch processing can help in managing how data is processed, it does not directly contribute to correcting or adapting the model itself as conditions change. Model simplification may help in reducing complexity for better interpretability or computation efficiency, but it does not inherently resolve the issue of drift. Data augmentation focuses on enhancing the training dataset with variations of existing samples, which is useful for improving model robustness but does not directly