# Deploy MLflow models with `ModelBuilder`
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You can deploy MLflow models to a SageMaker AI endpoint using Amazon SageMaker AI Model Builder. For more information about Amazon SageMaker AI Model Builder, see [Create a model in Amazon SageMaker AI with ModelBuilder](https://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works-modelbuilder-creation.html).

`ModelBuilder` is a Python class that takes a framework model or a user-specified inference specification and converts it to a deployable model. For more details about the `ModelBuilder` class, see [ModelBuilder](https://sagemaker.readthedocs.io/en/stable/api/inference/model_builder.html#sagemaker.serve.builder.model_builder.ModelBuilder).

To deploy your MLflow model using `ModelBuilder`, provide a path to your MLflow artifacts in the `model_metadata["MLFLOW_MODEL_PATH"]` attribute. Read on for more information about valid model path input formats:

**Note**  
If you provide your model artifact path in the form of an MLflow run ID or MLflow model registry path, then you must also specify your tracking server ARN through the `model_metadata["MLFLOW_TRACKING_ARN"]` attribute.
+ [Model paths that require an ARN in the `model_metadata`](#mlflow-track-experiments-model-deployment-with-arn)
+ [Model paths that do not require an ARN in the `model_metadata`](#mlflow-track-experiments-model-deployment-without-arn)

## Model paths that require an ARN in the `model_metadata`
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The following model paths do require that you specify an ARN in the `model_metadata` for deployment:
+ MLflow [run ID](https://mlflow.org/docs/latest/python_api/mlflow.entities.html?highlight=mlflow%20info#mlflow.entities.RunInfo.run_id): `runs:/aloy-run-id/run-relative/path/to/model`
+ MLflow [model registry path](https://mlflow.org/docs/latest/model-registry.html#find-registered-models): `models:/model-name/model-version`

## Model paths that do not require an ARN in the `model_metadata`
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The following model paths do not require that you specify an ARN in the `model_metadata` for deployment:
+ Local model path: `/Users/me/path/to/local/model`
+ Amazon S3 model path: `s3://amzn-s3-demo-bucket/path/to/model`
+ Model package ARN: `arn:aws:sagemaker:region:account-id:mlflow-tracking-server/tracking-server-name`

For more information on how MLflow model deployment works with Amazon SageMaker AI, see [Deploy MLflow Model to Amazon SageMaker AI](https://mlflow.org/docs/latest/deployment/deploy-model-to-sagemaker.html) in the MLflow documentation.

If using an Amazon S3 path, you can find the path of your registered model with the following commands:

```
registered_model = client.get_registered_model(name='AutoRegisteredModel')
source_path = registered_model.latest_versions[0].source
```

The following sample is an overview of how to deploy your MLflow model using `ModelBuilder` and an MLflow model registry path. Because this sample provides the model artifact path in the form of an MLflow model registry path, the call to `ModelBuilder` must also specify a tracking server ARN through the `model_metadata["MLFLOW_TRACKING_ARN"]` attribute.

**Important**  
You must use version [2.224.0](https://pypi.org/project/sagemaker/2.224.0/) or later of the SageMaker Python SDK to use `ModelBuilder`.

**Note**  
Use the following code example for reference. For end-to-end examples that show you how to deploy registered MLflow models, see [MLflow tutorials using example Jupyter notebooks](mlflow-tutorials.md).

```
from sagemaker.serve import ModelBuilder
from sagemaker.serve.mode.function_pointers import Mode
from sagemaker.serve import SchemaBuilder

my_schema = SchemaBuilder(
    sample_input=sample_input, 
    sample_output=sample_output
)

model_builder = ModelBuilder(
    mode=Mode.SAGEMAKER_ENDPOINT,
    schema_builder=my_schema,
    role_arn="Your-service-role-ARN",
    model_metadata={
        # both model path and tracking server ARN are required if you use an mlflow run ID or mlflow model registry path as input
        "MLFLOW_MODEL_PATH": "models:/sklearn-model/1"
        "MLFLOW_TRACKING_ARN": "arn:aws:sagemaker:region:account-id:mlflow-tracking-server/tracking-server-name"
    }
)
model = model_builder.build()
predictor = model.deploy( initial_instance_count=1, instance_type="ml.c6i.xlarge" )
```

To maintain [lineage tracking](https://docs.aws.amazon.com/sagemaker/latest/dg/lineage-tracking.html) for MLflow models deployed using `ModelBuilder`, you must have the following IAM permissions:
+ `sagemaker:CreateArtifact`
+ `sagemaker:ListArtifacts`
+ `sagemaker:AddAssociation`
+ `sagemaker:DescribeMLflowTrackingServer`

**Important**  
Lineage tracking is optional. Deployment succeeds without the permissions related to lineage tracking. If you do not have the permissions configured, you will see a lineage tracking permissions error when calling `model.deploy()`. However, the endpoint deployment still succeeds and you can directly interact with your model endpoint. If the permissions above are configured, lineage tracking information is automatically created and stored.

For more information and end-to-end examples, see [MLflow tutorials using example Jupyter notebooks](mlflow-tutorials.md).