Merge pull request #98269 from trevorbye/master

adding register model details

Author: PRMerger10

articles/machine-learning/service/how-to-train-ml-models.md

@@ -122,6 +122,16 @@ run = experiment.submit(estimator)
 print(run.get_portal_url())
 ```
 
+## Registering a model
+
+Once you've trained the model, you can save and register it to your workspace. Model registration lets you store and version your models in your workspace to simplify [model management and deployment](concept-model-management-and-deployment.md).
+
+Running the following code will register the model to your workspace, and will make it available to reference by name in remote compute contexts or deployment scripts. See [`register_model`](https://docs.microsoft.com/python/api/azureml-core/azureml.core.run.run?view=azure-ml-py#register-model-model-name--model-path-none--tags-none--properties-none--model-framework-none--model-framework-version-none--description-none--datasets-none--sample-input-dataset-none--sample-output-dataset-none--resource-configuration-none----kwargs-) in the reference docs for more information and additional parameters.
+
+```python
+model = run.register_model(model_name='sklearn-sample')
+```
+
 ## GitHub tracking and integration
 
 When you start a training run where the source directory is a local Git repository, information about the repository is stored in the run history. For more information, see [Git integration for Azure Machine Learning](concept-train-model-git-integration.md).