Add run id when registering ml azure model via python pipeline

Question

1

I have registed a model in this way:

from azureml.core.model import Model
model = Model.register(model_path="sklearn_regression_model.pkl",
                      model_name="sklearn_regression_model",
                      tags={'area': "diabetes", 'type': "regression"},
                      description="Ridge regression model to predict diabetes",
                      workspace=ws)

However I would like to add run id, from the experiment, so I can always back-track the model to the experiment that created the model. In azure ml there is a column indicating that it is possible to add run id to a registered model, however the model class doesn't have this parameter.

Answer 1

Create the following resources in order to execute an ML pipeline:

• Create a datastore that will be used to access the data required in the pipeline phases.
• Configure a Dataset object to point to persistent data stored in or accessible through a datastore. For temporary data transmitted between pipeline phases, create an OutputFileDatasetConfig object.
• Set up the compute targets where your pipeline stages will be executed.