mmodate the following MLflow deployment scenarios:Scenario 1: This scenario requires that you use a local Artifact Store alongside the MLflow Experiment Tracker in your ZenML stack. The local Artifact Store comes with limitations regarding what other types of components you can use in the same stack. This scenario should only be used to run ZenML locally and is not suitable for collaborative and production settings. No parameters need to be supplied when configuring the MLflow Experiment Tracker, e.g:

# Register the MLflow experiment tracker

zenml experiment-tracker register mlflow_experiment_tracker --flavor=mlflow

# Register and set a stack with the new experiment tracker

zenml stack register custom_stack -e mlflow_experiment_tracker ... --set

Scenario 5: This scenario assumes that you have already deployed an MLflow Tracking Server enabled with proxied artifact storage access. There is no restriction regarding what other types of components it can be combined with. This option requires authentication-related parameters to be configured for the MLflow Experiment Tracker.

Due to a critical severity vulnerability found in older versions of MLflow, we recommend using MLflow version 2.2.1 or higher.

Databricks scenario: This scenario assumes that you have a Databricks workspace, and you want to use the managed MLflow Tracking server it provides. This option requires authentication-related parameters to be configured for the MLflow Experiment Tracker.

Infrastructure Deployment

The MLflow Experiment Tracker can be deployed directly from the ZenML CLI:

# optionally assigning an existing bucket to the MLflow Experiment Tracker

zenml experiment-tracker deploy mlflow_tracker --flavor=mlflow -x mlflow_bucket=gs://my_bucket --provider=<YOUR_PROVIDER>