mlflow.azureml

mlflow.azureml.deploy(app_name, model_path, run_id, mlflow_home)

Deploy MLflow model to Azure ML.

Note

  • This command must be called from a console launched from Azure ML Workbench. Caller is reponsible for setting up Azure ML environment and accounts.
  • Azure ML can not handle any Conda environment. In particular the Python version is fixed. If the model contains Conda environment and it has been trained outside of Azure ML, the Conda environment might need to be edited to work with Azure ML.
Parameters:
  • app_name – Name of the deployed application.
  • model_path – Local or MLflow-run-relative path to the model to be exported.
  • run_id – If provided, run_id is used to retrieve the model logged with MLflow.
mlflow.azureml.export(output, model_path, run_id, mlflow_home)

Export MLflow model as Azure ML compatible model ready to be deployed.

Export MLflow model with everything needed to deploy on Azure ML. Output includes sh script with command to deploy the generated model to Azure ML.

Note

  • This command does not need an Azure ML environment to run.
  • Azure ML can not handle any Conda environment. If the model contains Conda environment and it has been trained outside of Azure ML, the Conda environment might need to be edited.
Parameters:
  • output – Output folder where the model is going to be exported to.
  • model_path – Local or MLflow run relative path to the model to be exported.
  • run_id – If provided, run_id is used to retrieve model logged with MLflow.