A machine learning (ML) specialist is administering a production Amazon SageMaker endpoint with model monitoring configured. Amazon SageMaker Model Monitor detects violations on the SageMaker endpoint, so the ML specialist retrains the model with the latest dataset. This dataset is statistically representative of the current production traffic. The ML specialist notices that even after deploying the new SageMaker model and running the first monitoring job, the SageMaker endpoint still has violations.
What should the ML specialist do to resolve the violations?

Question

A machine learning (ML) specialist is administering a production Amazon SageMaker endpoint with model monitoring configured. Amazon SageMaker Model Monitor detects violations on the SageMaker endpoint, so the ML specialist retrains the model with the latest dataset. This dataset is statistically representative of the current production traffic. The ML specialist notices that even after deploying the new SageMaker model and running the first monitoring job, the SageMaker endpoint still has violations.

What should the ML specialist do to resolve the violations?

Rocco Cristofaro · Accepted Answer

Run the Model Monitor baseline job again on the new training set. Configure Model Monitor to use the new baseline.

Rocco Cristofaro · Answer

Manually trigger the monitoring job to re-evaluate the SageMaker endpoint traffic sample.

Rocco Cristofaro · Answer

Delete the endpoint and recreate it with the original configuration.

Rocco Cristofaro · Answer

Retrain the model again by using a combination of the original training set and the new training set.

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