A custom model predicting user churn rate, using Vertex AI and its Model Monitoring for skew detection, is improved by splitting the training data (demographic and behavioral features) into two models. The challenge is to configure a new monitoring pipeline to manage these two models effectively.
The suggested answer is B. This approach is preferred due to its efficiency in minimizing management effort by using a single endpoint and monitoring job while still capturing the necessary information for the two different models through a configuration file.
You developed a custom model by using Vertex AI to predict your application's user churn rate. You are using Vertex AI Model Monitoring for skew detection. The training data stored in BigQuery contains two sets of features - demographic and behavioral. You later discover that two separate models trained on each set perform better than the original model. You need to configure a new model monitoring pipeline that splits traffic among the two models. You want to use the same prediction-sampling-rate and monitoring-frequency for each model. You also want to minimize management effort. What should you do?
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