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AWS SageMaker Integration: ModelOp Center provides seamless integration with AWS SageMaker to allow data scientists to design, build, test, and run models in SageMaker, while providing enterprise-wide model governance and life cycle management. Specific features include:
Model-Centric Inventory:
Inventory: Provides 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 allows for importing 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 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 modelorchestrates the entire life cycle of a SageMaker model, inclusive of all automated testing, SecOps reviews, compliance/governance reviews, as well as deployment of a SageMaker model to a SageMaker endpoint.
Model Risk: provides a detailed audit trail of every step in a Sagemaker’s SageMaker’s model lifecycle—from registration, to approvals, to tests, to deployment into Production and on-going monitoring.
Model Execution:
Execute Enables execution of a Sagemaker transform job from ModelOp Center manually and via a Model Life Cycle (MLC).
Provide Provides visibility into all Sagemaker deployments via the central ModelOp Center command centerthe operational status of all SageMaker endpoint deployments--and alert upon any issues--as part of an enterprise-wide operational control across all model runtimes.
Monitoring/Metrics:
Parse Parses and persists metrics from a Sagemaker SageMaker training job.
Execute Allows execution of back testing, drift and concept drift monitoring for Sagemaker deployed models (data in s3) via a Model Life Cycle (MLC) orchestration.Threshold, volumetrics, ethical fairness testing, and stability testing for SageMaker deployed models automatically.
Enables threshold-based notifications and alerting, with integrations into ServiceNow, JIRA, etcand other operational systems.
Monitor:
Monitoring Automation
OOTB Monitors
Custom Monitors
Alerting with Custom Thresholds
OOTB Remediation Paths
Integrated scheduling