Question 30

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.

 

References

• https://developers.google.com/machine-learning/guides/rules-of-ml/#rule_37_measure_trainingserving_skew
• https://cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning#challenges
• https://cloud.google.com/vertex-ai/docs/model-monitoring/using-model-monitoring
• https://machinelearningmastery.com/introduction-to-regularization-to-reduce-overfitting-and-improve-generalization-error/