@Vittal Pai​  - In general, please follow the below steps for the mlflow CLI error,

Step 1: set up API token and create secrets as mentioned in the below document

https://docs.databricks.com/machine-learning/manage-model-lifecycle/multiple-workspaces.html#set-up-...

Step 2: embed the below piece of code at the top of your notebook

import os
os.environ["DATABRICKS_TOKEN"] = access_token
os.environ["DATABRICKS_HOST"] = "https://<workspace-url>;"
access_token = dbutils.secrets.get(scope="{<secret-name>}", key="mlflow-access-token")
from databricks_cli.configure import provider
config_provider = provider.EnvironmentVariableConfigProvider()
provider.set_config_provider(config_provider)
 

In case you face an issue, Please ensure the token is refreshed, as the token has a shell life.