Configure a daily Dataflow job in Cloud Composer to compute the success metric, ensuring comprehensive data analysis but potentially increasing costs due to frequent computations.

Implement Vertex AI Model Monitoring to identify input feature skew with a 100% sample rate and a two-day monitoring interval, providing a balance between detection speed and computational efficiency.

Establish a weekly cron job in Cloud Tasks to retrain the model before the newsletter is compiled, which might lead to unnecessary retraining if the model's performance hasn't significantly declined.

Arrange for a weekly query in BigQuery to assess the success metric, which is cost-effective but may not promptly identify subtle changes in data distribution affecting model performance.

Combine Vertex AI Model Monitoring for early detection of performance degradation with a weekly BigQuery query for cost-effective success metric assessment, ensuring both proactive performance tracking and cost efficiency.