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

The correct answer is A. Create alerts to monitor for skew, and retrain the model.. 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- m

Submitted by mateo_ar· Apr 18, 2026Monitoring, optimizing, and maintaining ML solutions

Question

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?

Options

ACreate alerts to monitor for skew, and retrain the model.
BPerform feature selection on the model, and retrain the model with fewer features.
CRetrain the model, and select an L2 regularization parameter with a hyperparameter tuning
DPerform feature selection on the model, and retrain the model on a monthly basis with fewer

How the community answered

(35 responses)

A
77% (27)
B
3% (1)
C
6% (2)
D
14% (5)

Explanation

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

Topics

#Data drift#Model monitoring#Model retraining

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