@bhogasena reddy kalakata Thanks for the question. Can you please share the notebook that you are trying. Here is the sample for ml flow tracking.
Set MLflow Tracking to only track in your Azure Machine Learning workspace
Hi Team,
As per below link
And section: Set MLflow Tracking to only track in your Azure Machine Learning workspace
We can set ML Flow Tracking URI to Azure ML in Databricks but i am getting below error upon setting and trying to create experiement
UnsupportedModelRegistryStoreURIException: Model registry functionality is unavailable; got unsupported URI 'azureml://eastus.api.azureml.ms/mlflow/v1.0/subscriptions/60732f07-e07d-4492-b2cc-e43155932aca/resourceGroups/RG-BHOGA/providers/Microsoft.MachineLearningServices/workspaces/[REDACTED]' for model registry data storage. Supported URI schemes are: ['', 'file', 'databricks', '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.
Could you please advise what has to be done?
Azure Machine Learning
Azure Databricks
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Facundo Santiago 1 Reputation point Microsoft Employee
2022-07-26T18:41:36.383+00:00 @bhogasena reddy kalakata , you probably forgot to install the plugin azureml-mlflow. This is required to interpret the URI and that's why you get the unsupported URI error.