What’s New:
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 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: 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 artifacts.
Model Risk / Governance:
Productionization: orchestrates 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 model lifecycle—from registration, to approvals, to tests, to deployment into Production and on-going monitoring.
Model Execution:
Enables execution of a Sagemaker transform job from ModelOp Center manually and via a Model Life Cycle (MLC).
Provides visibility into the 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:
Parses and persists metrics from a SageMaker training job.
Allows execution of back testing, drift and concept drift monitoring, volumetrics, ethical fairness testing, and stability testing for SageMaker deployed models automatically.
Enables threshold-based notifications and alerting, with integrations into ServiceNow, JIRA, and other operational systems.
Monitoring Updates
ModelOp Center v2.4 introduces a new comprehensive monitoring package designed to help enterprises implement monitoring across ALL models, regardless of the language, framework, model runtime (e.g. SageMaker, Spark, Docker, etc.), or environment (on-prem, cloud, or hybrid). Specific features include:
...