You have recently deployed a machine learning model to a Vertex AI endpoint, and due to the frequent data drifts, you have enabled both request-response logging and created a Vertex AI Model Monitoring job to ensure the model's performance. However, you've noticed that the model is receiving higher-than-expected traffic, leading to increased costs. Your goal is to reduce these monitoring costs while still being able to quickly detect any data drift. How should you adjust your current setup to achieve this?