You recently developed a custom ML model that was trained in Vertex AI on a post-processed training dataset stored in BigQuery. You used a Cloud Run container to deploy the prediction service. The service performs feature lookup and pre-processing and sends a prediction request to a model endpoint in Vertex AI. You want to configure a comprehensive monitoring solution for training-serving skew that requires minimal maintenance. What should you do?

Vertex AI Model Monitoring is a fully managed solution for monitoring training-serving skew that, by definition, requires minimal maintenance. Using the console for diagnostics is recommended for a comprehensive monitoring solution because there could be multiple causes for the skew that require manual review. Creating custom dashboards or scheduling Dataflow jobs with TFDataValidation adds maintenance overhead. Autoretraining on alerts is not recommended since skew might be due to preprocessing errors that retraining won't solve.