How should you address the input differences in production?

Your team trained and tested a DNN regression model with good results. Six months after deployment, the model is performing poorly due to a change in the distribution of the input data.

How should you address the input differences in production?
A . Create alerts to monitor for skew, and retrain the model.
B . Perform feature selection on the model, and retrain the model with fewer features
C . Retrain the model, and select an L2 regularization parameter with a hyperparameter tuning service
D . Perform feature selection on the model, and retrain the model on a monthly basis with fewer features

Answer: A

Explanation:

Data drift doesn’t necessarily require feature reselection (e.g. by L2 regularization). https://cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning#challenges

Data values skews: These skews are significant changes in the statistical properties of data, which means that data patterns are changing, and you need to trigger a retraining of the model to capture these changes. https://developers.google.com/machine-learning/guides/rules-of-ml/#rule_37_measure_trainingserving_skew