# MLflow

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](https://docs.fiddler.ai/integrations/ml-platforms-and-tools/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
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
# 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.