Connection errors when connecting locally from VSC to Azure ML via MLflow tracking server

Question

I'm experiencing problems when following the basic instructions from Azure ML docs to work locally on Python notebooks and track them in Azure ML via MLflow.

I'm using a conda environment, it's activated and used as kernel in VSC.

All modules from the docs are installed.

I also have the config.json file in the same folder as the notebook.

Azure ML standard workspace is selected in VSC (I think authentication is also okay, because i can see the whole menu tree from Azure ML in VSC)

I'm on a Mac

https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow-configure-tracking?view=azureml-api-2&tabs=cli%2Cmlflow

https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow-cli-runs?view=azureml-api-2&tabs=interactive%2Ccli

When running the follow code:

from azure.ai.ml import MLClient

from azure.identity import DefaultAzureCredential

ml_client = MLClient.from_config(credential=DefaultAzureCredential())

mlflow_tracking_uri = ml_client.workspaces.get(ml_client.workspace_name).mlflow_tracking_uri

I get this error:

DefaultAzureCredential failed to retrieve a token from the included credentials. Attempted credentials: EnvironmentCredential: EnvironmentCredential authentication unavailable. Environment variables are not fully configured. Visit https://aka.ms/azsdk/python/identity/environmentcredential/troubleshoot to troubleshoot.this issue.

And further on:

Content: {"error":"invalid_grant","error_description":"AADSTS700082: The refresh token has expired due to inactivity. The token was issued on 2020-10-29T05:28:47.8757835Z and was inactive for 90.00:00:00.\r\nTrace ID: 3f2221fa-0d29-4934-893e-72c565a44a00\r\nCorrelation ID: dc908d2c-927e-4c55-a605-8e36bfcd0467\r\nTimestamp: 2023-05-08 14:41:48Z","error_codes":[700082],"timestamp":"2023-05-08 14:41:48Z","trace_id":"3f2221fa-0d29-4934-893e-72c565a44a00","correlation_id":"dc908d2c-927e-4c55-a605-8e36bfcd0467","error_uri":"https://login.microsoftonline.com/error?code=700082"} To mitigate this issue, please refer to the troubleshooting guidelines here at https://aka.ms/azsdk/python/identity/defaultazurecredential/troubleshoot.

Thereafter I just set the tracking URI by copying it from the workspace. Then when running:

mlflow_tracking_uri = 'azureml://westeurope.api.azureml.ms/mlflow/v1.0/subscriptions/bb026f63-bb96-4a45-9ab1-aed3ded1f99e/resourceGroups/OSAP-Student-Env/providers/Microsoft.MachineLearningServices/workspaces/demo_workspace'

import mlflow

mlflow.set_tracking_uri(mlflow_tracking_uri)

mlflow.set_experiment(experiment_name='experiment_with_mlflow')

I get this error:

UnsupportedModelRegistryStoreURIException: Model registry functionality is unavailable; got unsupported URI 'azureml://westeurope.api.azureml.ms/mlflow/v1.0/subscriptions/bb026f63-bb96-4a45-9ab1-aed3ded1f99e/resourceGroups/OSAP-Student-Env/providers/Microsoft.MachineLearningServices/workspaces/demo_workspace' for model registry data storage. Supported URI schemes are: ['', 'file', 'databricks', 'databricks-uc', 'http', 'https', 'postgresql', 'mysql', 'sqlite', 'mssql']. See https://www.mlflow.org/docs/latest/tracking.html#storage for how to run an MLflow server against one of the supported backend storage locations.

Why is the standard code not working? I really have no clue what's wrong here. Does anybody experienced the same or knows how to solve this issue?

Many thanks for the person(s) who can help me!:)