w_bucket=gs://my_bucket --provider=<YOUR_PROVIDER>You can pass other configurations specific to the stack components as key-value arguments. If you don't provide a name, a random one is generated for you. For more information about how to work use the CLI for this, please refer to the dedicated documentation section.

Authentication Methods

You need to configure the following credentials for authentication to a remote MLflow tracking server:

tracking_uri: The URL pointing to the MLflow tracking server. If using an MLflow Tracking Server managed by Databricks, then the value of this attribute should be "databricks".

tracking_username: Username for authenticating with the MLflow tracking server.

tracking_password: Password for authenticating with the MLflow tracking server.

tracking_token (in place of tracking_username and tracking_password): Token for authenticating with the MLflow tracking server.

tracking_insecure_tls (optional): Set to skip verifying the MLflow tracking server SSL certificate.

databricks_host: The host of the Databricks workspace with the MLflow-managed server to connect to. This is only required if the tracking_uri value is set to "databricks". More information: Access the MLflow tracking server from outside Databricks

Either tracking_token or tracking_username and tracking_password must be specified.

This option configures the credentials for the MLflow tracking service directly as stack component attributes.

This is not recommended for production settings as the credentials won't be stored securely and will be clearly visible in the stack configuration.

# Register the MLflow experiment tracker

zenml experiment-tracker register mlflow_experiment_tracker --flavor=mlflow \

--tracking_uri=<URI> --tracking_token=<token>

# You can also register it like this:

# zenml experiment-tracker register mlflow_experiment_tracker --flavor=mlflow \

#    --tracking_uri=<URI> --tracking_username=<USERNAME> --tracking_password=<PASSWORD>

# Register and set a stack with the new experiment tracker