[elenasamuylova]

Depends on the use case and available instrumentation!

If the feedback is available almost instantly (e.g. you recommend something to a user based on a model prediction and you know if they clicked on it or not), you can log the user action in your data warehouse to have the ground truth easily available for further analysis. Then you run the performance reports on top of complete logs.

In other cases you might have to wait for the ground truth (e.g. you predict the demand for some future period and then wait for it to materialize, or you need to label the data first). In this case you can log the ground truth to the data warehouse once it becomes available and join with the prediction logs. You can then run complete performance monitoring with error analysis as a batch job. In the meantime, you can still monitor the data drift.

Could you describe a specific use case and environment? We can brainstorm how to best arrange it.