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Documentation Index

Fetch the complete documentation index at: https://handbook.fiddler.ai/llms.txt

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Model artifact upload and deployment status. This enum tracks the status of model artifacts in Fiddler, indicating whether explainability features are available and what type of model deployment is active. Artifact Types:
  • No Model: No artifacts uploaded, monitoring only
  • Surrogate: Fiddler-generated surrogate model for explainability
  • User Uploaded: User-provided model artifacts for full explainability

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Examples

Checking artifact status and capabilities: 
# Check current artifact status
model = fdl.Model.from_name('my_model', project_id=project.id)

if model.artifact_status == fdl.ArtifactStatus.NO_MODEL:
    print("Monitoring only - no explainability features")

elif model.artifact_status == fdl.ArtifactStatus.SURROGATE:
    print("Surrogate model available - basic explainability")

elif model.artifact_status == fdl.ArtifactStatus.USER_UPLOADED:
    print("Full model artifacts - complete explainability")

    # Upload model artifacts to enable explainability
    if model.artifact_status == fdl.ArtifactStatus.NO_MODEL:

        job = model.add_artifact(
            model_dir='./model_package/',
            deployment_params=fdl.DeploymentParams(
                    artifact_type=fdl.ArtifactType.PYTHON_PACKAGE
            )
        )
        job.wait()
Artifact status affects available explainability features. User-uploaded artifacts provide the most comprehensive explanation capabilities.

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NO_MODEL = ‘no_model’

No model artifacts have been uploaded. The model exists in Fiddler for monitoring purposes only. Data drift detection, performance monitoring, and alerting are available, but explainability features are not accessible. Available features:
  • Data drift monitoring
  • Performance metric tracking
  • Alert rule configuration
  • Dashboard visualization
  • Data publishing and monitoring
Unavailable features:
  • Point explainability
  • Global feature importance
  • Model artifact-based analysis
  • Custom explanation methods
This is the default status for newly created models before any artifacts are uploaded.

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SURROGATE = ‘surrogate’

Surrogate model generated by Fiddler for explainability. Fiddler has automatically generated a surrogate model based on your published data to provide basic explainability features. The surrogate model approximates your original model’s behavior. Available features:
  • Basic point explainability
  • Global feature importance
  • Approximated explanations
  • All monitoring features
Characteristics:
  • Automatically generated by Fiddler
  • Approximates original model behavior
  • Provides reasonable explanation quality
  • No additional setup required
Limitations:
  • May not perfectly match original model
  • Limited to surrogate model capabilities
  • Cannot use custom explanation methods

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USER_UPLOADED = ‘user_uploaded’

User-provided model artifacts have been uploaded. Complete model artifacts have been uploaded, enabling full explainability features with the actual model. This provides the highest quality explanations and complete feature access. Available features:
  • Full point explainability with actual model
  • Global feature importance from actual model
  • Custom explanation methods (if defined)
  • Model artifact-based analysis
  • All monitoring and surrogate features
Characteristics:
  • Uses actual uploaded model
  • Highest explanation accuracy
  • Supports custom explanation methods
  • Complete feature access
Requirements:
  • Model artifacts must be properly packaged
  • Compatible with Fiddler’s deployment environment
  • May require specific Python dependencies