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Viewing a factsheet for a tracked asset
Last updated: Feb 11, 2025
Viewing a factsheet for a tracked asset

Review the details that are captured for each tracked asset in a factsheet associated with an AI use case. You can also print a report to share or archive the factsheet details.

What is captured in a factsheet?

From the point where you start tracking an asset in an AI use case, facts are collected in a factsheet for the asset. As the asset moves from one phase of the lifecycle to the next, the facts are added to the appropriate section.

Important: The factsheet records the most recent activity in any category. For example, if you evaluate a model in a pre-production space, and then evaluate it in a production space, the details from the production evaluation are captured in the factsheet, over-writing the previous data. Thus, the factsheet maintains a complete record of the current state of the asset.

Tracking details

When tracking is enabled, the recording of facts is triggered by actions on the tracked asset. Similarly, changes in the AI lifecycle control where the asset displays in an AI use case, to show the progression from development to operation. For example, when an asset is saved in a project and tracking is enabled, the asset displays in the Develop phase. When an asset is evaluated, whether in a project or space, the use case is updated to show the asset and all evaluation data in the Validate phase. Deployments in production display in the Operate phase.

Model location Develop Validate Operate
Project ✓
Preproduction space ✓
Preproduction space ✓
Production space ✓
Note: If you created AI use cases in a previous version, factsheet data that was listed in the Test phase is now listed in the Validation phase. This reflects an emphasis on better reflecting the asset lifecycle rather than focusing on whether an evaluation was conducted in a project or space.

Viewing a factsheet for a machine learning model

The model factsheet displays all of the details for a machine learning model.

The details in the factsheet depend on the type of model. For example, this model trained by using AutoAI displays:

  • General model details such as name, model ID, type of model and software specification used. The input schema shows the details for the model structure.

    General model details

  • Training information including the associated project, training data source, and hybrid pipeline name.

    Training details

  • General deployment details, such as deployment name, deployment ID, and software specification used.

    General deployment details

  • Evaluation details for your model, including:

    • Payload logging and Feedback data information
    • Quality metrics after evaluating the deployment
    • Fairness metrics that test for bias in monitored groups
    • Drift and drift v2 details following an evaluation
    • Global explainability details
    • Metrics information from custom monitors
    • Model health details about scoring requests to online deployments, including payload size, records, scoring requests, throughput and latency, and users
Note:

If integration with IBM OpenPages is enabled, these facts are shared with OpenPages.

  • Payload logging and Feedback data information
  • Quality metrics after evaluating the deployment
  • Fairness metrics that test for bias in monitored groups
  • Drift details following an evaluation
  • Global explainability details
  • Metrics information from custom monitors
  • Model health details about scoring requests to online deployments, including payload size, records, scoring requests, throughput and latency, and users

Next steps

Click Export report to save a report of the factsheet.

Parent topic: Governing assets in AI use cases