You've created a custom model in Vertex AI to predict user churn rate for your application. Vertex AI Model Monitoring is used for skew detection, and your training data in BigQuery includes two types of features: demographic and behavioral. Recently, you found that two separate models, each trained on one of these feature sets, outperform the original model. Now, you want to set up a new model monitoring pipeline that directs traffic to both models while maintaining consistent prediction-sampling rates and monitoring frequencies. You also aim to minimize management overhead. What should be your approach?