> ## Documentation Index
> Fetch the complete documentation index at: https://docs.fiddler.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# ArtifactStatus

> Model artifact upload and deployment status.

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

## Examples

Checking artifact status and capabilities:

```python theme={null}
# 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()
```

<Info>
  Artifact status affects available explainability features. User-uploaded
  artifacts provide the most comprehensive explanation capabilities.
</Info>

## 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.

## 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

## 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