1 year ago

https://aws.amazon.com/polly/ As you move your machine learning (ML) workloads into production, you need to continuously monitor your deployed models and iterate when you observe a deviation in your model performance. When you build a new model, you typically start validating the model offline using historical inference request data.

Deploying a model in shadow mode lets you conduct a more holistic test by routing a copy of the live inference requests for a production model to the new (shadow) model.

Once you complete a shadow test, you can use the https://docs.aws.amazon.com/sagemaker/latest/dg/deployment-guardrails.html for SageMaker inference endpoints to safely update your model in production.

On the SageMaker console, select Inference and Shadow tests to create, monitor, and deploy shadow tests.