Amazon SageMaker AI

Description

SageMaker AI provides managed infrastructure for the model lifecycle from training through hosted inference endpoints. Shadow testing deploys a challenger model version alongside the production champion and mirrors a portion of real requests to the shadow variant without returning shadow responses to users. SageMaker Model Monitor watches deployed models in production, using rules to detect drift in data and model quality and alerting operators when deviations occur.

Solution

SageMaker AI is a managed ML platform rather than an agent loop. Models are trained and deployed to inference endpoints. Shadow testing routes a copied portion of live inference requests to a shadow fleet and compares metrics without serving shadow responses. Model Monitor runs scheduled or continuous monitoring jobs that compare captured production inputs and outputs against a baseline and raise alerts on drift.

Primary use cases

• training and hosting machine learning models
• shadow testing a challenger model against the champion
• production monitoring of model and data quality drift