Question

A machine learning team runs a production SageMaker real-time endpoint serving a recommendation model. They need to deploy a new model version but are concerned that a faulty version could cause a spike in inference errors affecting customers. They want SageMaker to automatically shift traffic to the new version gradually, monitor CloudWatch metrics during the shift, and automatically roll back to the previous version if error thresholds are breached. Which approach should they configure?

Accepted Answer

Use a blue/green deployment with canary traffic shifting and CloudWatch alarm-based auto-rollback in the endpoint deployment configuration

Answer

Create a second endpoint with the new model and manually switch the application's endpoint name after testing

Answer

Deploy the new model as a production variant on the same endpoint and manually adjust variant weights over several days

Answer

Use a batch transform job to validate the new model on historical data before replacing the endpoint