Manually evaluate the loss performance of each model using a held-out dataset every month, ensuring the dataset is updated to reflect current product trends.

Use the validation dataset to assess the loss performance of each model, adjusting the validation set periodically to account for new product categories.

Implement the What-If Tool to visually compare the receiver operating characteristic (ROC) curves for each model, focusing on areas with high classification uncertainty.

Leverage the Continuous Evaluation feature in AI Platform to automatically compare the mean average precision across the models, enabling real-time performance tracking and alerts for significant deviations.

Combine both manual evaluation of loss performance on a held-out dataset and the use of the What-If Tool for ROC curve comparison to ensure comprehensive performance monitoring.