Creating MLFlow Deployments

With the MLFlow integration set up, you can create Deployments that typically involves the steps outlined in Creating a Deployment/. For SageMaker deployments, using MLFlow is not supported. For Azure Machine Learning deployments review the Azure Machine Learning documentation on how to use the model registry.

Prerequisites

• You added a Repository that adheres to the requirements. Note that Repositories used for MLFlow Deployments must use the reference system.
• (Optional) include Blob details, as illustrated in this example:

{
    "reference": {
        "mlflow": {
            "model": "my-model",
            "stage": "Production",
            "blob": {
                "region": "eu-central-1"
            }
        }
    } 
}

• Either stage or version can be present (not both), where version is a number in the form of a string, e.g. "5".

Explainer

We recommend to deploy an explainer using MLFlow, however also provide the option to for instance use a custom docker image as explainer. When using MLFlow, we require the model and stage or version to be the same as configured in your model's reference.json. We will look for an explainer.dill file in the explainer artifact folder of your model's run that corresponds to the version or stage specified.

Updating Model

If you have configured your model using stage and transitioned a new model to that stage, you can update your model and explainer without needing to reconfigure your Deeploy settings. You can either update via the Deeploy application (press update in the details page) or call Deeploy's api to patch a deployment with an empty request body. See our swagger documentation for more information.

Example

For a full example, please check out this repository, which deploys a Scikit-learn model and SHAP explainer to Deeploy using MLFlow.