> ## 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.

# MLflow

> Explore how Fiddler helps your team onboard, monitor, explain, and analyze models with MLFlow. Learn to ingest model metadata and artifacts for observability.

Fiddler allows your team to onboard, monitor, explain, and analyze your models developed with [MLflow](https://mlflow.org/).

This guide shows you how to ingest the model metadata and artifacts stored in your MLflow model registry and use them to set up model observability in the Fiddler Platform:

1. Exporting Model Metadata from MLflow to Fiddler
2. Uploading Model Artifacts to Fiddler for XAI

### Onboarding a Model

Refer to this [section](/integrations/ml-platforms/databricks-integration#creating-the-fiddler-model) of the Databricks integration guide for onboarding your model to Fiddler using model information from MLflow.

### Uploading Model Artifacts

Using the [**MLflow API**](https://mlflow.org/docs/latest/python_api/mlflow.html) you can query the model registry and get the **model signature** which describes the inputs and outputs as a dictionary.

#### Uploading Model Files

Sharing your model artifacts helps Fiddler explain your models. By leveraging the MLflow API you can download these model files:

```python theme={null}
import os
import mlflow
from mlflow.store.artifact.models_artifact_repo import ModelsArtifactRepository

model_name = "example-model-name"
model_stage = "Staging"  # Should be either 'Staging' or 'Production'

mlflow.set_tracking_uri("databricks")
os.makedirs("model", exist_ok=True)
local_path = ModelsArtifactRepository(
    f'models:/{model_name}/{model_stage}'
).download_artifacts("", dst_path="model")

print(f'{model_stage} Model {model_name} is downloaded at {local_path}')
```

Once you have the model file, you can create a package.py file in this model directory that describes how to access this model.

Finally, you can upload all the model artifacts to Fiddler:

```python theme={null}
# Assumes an initialized Python client session and instantiated Model
job = model.add_artifact(
    model_dir=MODEL_ARTIFACTS_DIR,
)
job.wait()
```

Alternatively, you can skip uploading your model and use Fiddler to generate a surrogate model to get low-fidelity explanations for your model.

Please refer to the Explainability guide for detailed information on model artifacts, packages, and surrogate models.