AWS SageMaker AI assets

The AWS SageMaker AI integration of Collibra Platform uses the following asset types. The asset types are available out of the box in Collibra.

Important The functions of AWS SageMaker AI integration is limited if you do not enable AI governance. When enabled, you can add custom attributes or relations, such as AI Model Version, to the AWS SageMaker AI Model Version asset.

Tip  You can also integrate metadata from Amazon SageMaker Unified Studio, built on Amazon SageMaker Catalog. For more information, go to the Amazon SageMaker Unified Studio documentation.

AWS SageMaker AI object Asset type Description
Model package group; deployed model not in the Model Registry AI Base Model Represents foundational business concepts that define the model’s purpose and governing principles in your Amazon SageMaker workspace.
Model package with approval status Approved (Pending and Rejected packages are skipped); deployed model not in the Model Registry (synthetic version) AWS SageMaker AI Model Version

A subtype of AI Model Version that represents AI model versions in Amazon SageMaker.
Endpoint production variant, one per variant per endpoint AI Model Deployment The operational instance where a model version is assigned computational resources to execute and generate real-time outputs.
Endpoint AI Endpoint The access point for external systems that provides a consistent interface while allowing for the seamless exchange of underlying deployments.
Monitoring schedule AI Monitor The configuration of automated tracking parameters and alerting thresholds used to observe a deployment’s real-world performance and detect deviations from expected behavior.
Synthetic container named "/", one per deployment, holding that deployment's output files Storage Container Represents the S3 endpoint output paths Amazon SageMaker workspace. You can ingest this data during the integration by selecting Yes in the Ingest deployment output field.
S3 output location of a deployment File Represents the S3 endpoint output paths Amazon SageMaker workspace. You can ingest this data during the integration by selecting Yes in the Ingest deployment output field.

AWS SageMaker AI diagram view

For information on the data that is integrated from AWS SageMaker AI, go to Integrated AWS SageMaker AI data.

Steps

  1. Open the asset page.
  2. Click the Diagram tab.
    The diagram is shown in the default diagram view.
  3. Click to add a new view.
  4. Select the Text option below the diagram view name.
    The diagram view text editor is shown.
  5. Copy the code from the Show JSON code section below and paste it in the diagram view text editor.
  6. Click Save.
  7. Edit the name and description of the diagram view as needed.
Copy 
{
  "nodes": [
    {
      "id": "FoundationalAIModel",
      "type": {
        "id": "00000000-0000-0000-0000-000000031422"
      }
    },
    {
      "id": "AWSSagemakerAIModel",
      "type": {
        "id": "00000000-0000-0000-0000-000000031406"
      }
    },
    {
      "id": "AIModelDeployment",
      "type": {
        "id": "00000000-0000-0000-0000-000000031500"
      }
    },
    {
      "id": "AIEndpoint",
      "type": {
        "id": "00000000-0000-0000-0000-000000031501"
      }
    },
    {
      "id": "AIMonitor",
      "type": {
        "id": "00000000-0000-0000-0000-000000031502"
      }
    },
    {
      "id": "FileContainer",
      "type": {
        "id": "00000000-0000-0000-0001-002900000002"
      }
    },
    {
      "id": "File",
      "type": {
        "id": "00000000-0000-0000-0000-000000031304"
      }
    }
  ],
  "edges": [
    {
      "from": "FoundationalAIModel",
      "to": "AWSSagemakerAIModel",
      "label": "",
      "style": "arrow",
      "type": {
        "id": "00000000-0000-0000-0000-000000077000"
      },
      "roleDirection": true
    },
    {
      "from": "AWSSagemakerAIModel",
      "to": "AIModelDeployment",
      "label": "",
      "style": "arrow",
      "type": {
        "id": "00000000-0000-0000-0000-000000077001"
      },
      "roleDirection": true
    },
    {
      "from": "AIModelDeployment",
      "to": "AIEndpoint",
      "label": "",
      "style": "arrow",
      "type": {
        "id": "00000000-0000-0000-0000-000000077006"
      },
      "roleDirection": true
    },
    {
      "from": "AIModelDeployment",
      "to": "AIMonitor",
      "label": "",
      "style": "arrow",
      "type": {
        "id": "00000000-0000-0000-0000-000000077007"
      },
      "roleDirection": true
    },
    {
      "from": "AIModelDeployment",
      "to": "FileContainer",
      "label": "",
      "style": "arrow",
      "type": {
        "id": "00000000-0000-0000-0000-000000077004"
      },
      "roleDirection": true
    },
    {
      "from": "FileContainer",
      "to": "File",
      "label": "",
      "style": "arrow",
      "type": {
        "id": "00000000-0000-0000-0000-000000007060"
      },
      "roleDirection": true
    }
  ],
  "showOverview": false,
  "enableFilters": true,
  "showLabels": true,
  "showFields": true,
  "showLegend": true,
  "showPreview": true,
  "visitStrategy": "completeGraph",
  "layout": "HierarchyTopBottom",
  "maxNodeLabelLength": 50,
  "maxEdgeLabelLength": 30,
  "layoutOptions": {
    "compactGroups": false,
    "componentArrangementPolicy": "topmost",
    "edgeBends": true,
    "edgeBundling": true,
    "edgeToEdgeDistance": 5,
    "minimumLayerDistance": "auto",
    "nodeToEdgeDistance": 5,
    "orthogonalRouting": true,
    "preciseNodeHeightCalculation": true,
    "recursiveGroupLayering": true,
    "separateLayers": true,
    "webWorkers": true,
    "nodePlacer": {
      "barycenterMode": true,
      "breakLongSegments": true,
      "groupCompactionStrategy": "none",
      "nodeCompaction": false,
      "straightenEdges": true
    }
  }
}