@Dhineshkumar Currently, Azure ML supports mlflow for model management which can be used to register and query models using the mlflow client. Stages are assigned to a model's version (instead of models) which means that a given model can have multiple versions on different stages. You can use this documentation to refer the capabilities of the mlflow client. However, the following is also a current limitation.
Stages can only be accessed using the MLflow SDK. They don't show up in the Azure ML Studio portal and can't be retrieved using neither Azure ML SDK, Azure ML CLI, or Azure ML REST API. Creating deployment from a given model's stage is not supported by the moment.
Moving of Azure ML workspace from one resource group to another is currently not supported.
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