You are developing an ML model that predicts the cost of used automobiles based on data such as location, condition, model type, color, and engine/battery efficiency. The data is updated every…

Comparing only training and evaluation losses of the current run primarily checks for overfitting in the current run, but does not guarantee improvement over the previously deployed model, which is crucial for a cost-effective retraining workflow.

Comparing the current model's evaluation results against a previously deployed model (and considering whether training loss is too different from evaluation loss to avoid overfitting) allows for deploying only when there's a demonstrable performance improvement. This prevents unnecessary retraining or deployment of inferior models, directly minimizing computational costs associated with model deployment and potential rollback if a model regresses.

While comparing current and previous evaluation results is good, adding a specific 'more than 5%' threshold might unnecessarily delay deployment of models with valid, smaller improvements, potentially hindering timely updates.

This choice repeats A, which only checks for overfitting in the current run and doesn't consider the performance relative to the currently deployed model.