What’s New:
AWS SageMaker Integration:
Model-Centric Inventory:
Inventory: Provides a detailed, model-centric view of all elements, artifacts, jobs, deployments, etc. of a model developed in SageMaker, inclusive of all versions of a given model over time.
Register: import a model developed in SageMaker into ModelOp Center, including the model’s related Training Jobs, Batch Transform Jobs, Endpoint configurations, Endpoints. Provides automated syncing with the related Sagemaker artifacts.
Model Risk / Governance:
Productionization: deploy a Sagemaker model to a Sagemaker endpoint via an MLC orchestration. The detailed steps of deployment are persisted with the specific version of the model.
Model Risk: provides a detailed audit trail of every step in a Sagemaker’s model lifecycle—from registration, to approvals, to tests, to deployment into Production and on-going monitoring.
Model Execution:
Execute a Sagemaker transform job from ModelOp Center manually and via a Model Life Cycle (MLC)
Provide visibility into all Sagemaker deployments via the central ModelOp Center command center.
Monitoring/Metrics:
Parse and persists metrics from a Sagemaker training job.
Execute back testing, drift and concept drift monitoring for Sagemaker deployed models (data in s3) via a Model Life Cycle (MLC) orchestration.
Threshold-based notifications and alerting, with integrations into ServiceNow, JIRA, etc.
Monitor:
Monitoring Automation
OOTB Monitors
Custom Monitors
Alerting with Custom Thresholds
OOTB Remediation Paths
Integrated scheduling