You recently deployed a model to a Vertex AI endpoint. Your data drifts frequently, so you have enabled request-response logging and created a Vertex AI Model Monitoring job. You have observed that…

Replacing the monitoring job with a custom Dataflow pipeline would involve significant development and maintenance overhead, likely increasing overall cost and effort compared to adjusting an existing monitoring job parameter.

Replacing the monitoring job with a custom SQL script is a manual and less automated approach for drift detection, which could be less efficient and more costly to maintain than using Vertex AI Model Monitoring.

Decreasing the sample_rate parameter in the RandomSampleConfig of the monitoring job reduces the percentage of prediction requests and responses that are analyzed by Vertex AI Model Monitoring. This directly lowers the processing cost by reducing the amount of data processed while still allowing the monitoring job to run frequently enough to quickly detect drift.

Increasing the monitor_interval parameter would reduce the frequency of monitoring checks, directly slowing down the detection of data drift, which contradicts the requirement to 'quickly detect drift'.