Merge pull request #108026 from Blackmist/enhanced-registration

updating some examples to demonstrate setting metadata in tags

Author: PRMerger12

articles/machine-learning/how-to-deploy-and-where.md

@@ -64,7 +64,9 @@ A registered model is a logical container for one or more files that make up you
 > [!TIP]
 > When you register a model, you provide the path of either a cloud location (from a training run) or a local directory. This path is just to locate the files for upload as part of the registration process. It doesn't need to match the path used in the entry script. For more information, see [Locate model files in your entry script](#locate-model-files-in-your-entry-script).
 
-Machine learning models are registered in your Azure Machine Learning workspace. The model can come from Azure Machine Learning or from somewhere else. The following examples demonstrate how to register a model.
+Machine learning models are registered in your Azure Machine Learning workspace. The model can come from Azure Machine Learning or from somewhere else. When registering a model, you can optionally provide metadata about the model. The `tags` and `properties` dictionaries that you apply to a model registration can then be used to filter models.
+
+The following examples demonstrate how to register a model.
 
 ### Register a model from an experiment run
 
@@ -80,7 +82,9 @@ The code snippets in this section demonstrate how to register a model from a tra
   + Register a model from an `azureml.core.Run` object:
 
     ```python
-    model = run.register_model(model_name='sklearn_mnist', model_path='outputs/sklearn_mnist_model.pkl')
+    model = run.register_model(model_name='sklearn_mnist',
+                               tags={'area': 'mnist'},
+                               model_path='outputs/sklearn_mnist_model.pkl')
     print(model.name, model.id, model.version, sep='\t')
     ```
 
@@ -90,7 +94,8 @@ The code snippets in this section demonstrate how to register a model from a tra
 
     ```python
         description = 'My AutoML Model'
-        model = run.register_model(description = description)
+        model = run.register_model(description = description,
+                                   tags={'area': 'mnist'})
 
         print(run.model_id)
     ```
@@ -102,7 +107,7 @@ The code snippets in this section demonstrate how to register a model from a tra
 + **Using the CLI**
 
   ```azurecli-interactive
-  az ml model register -n sklearn_mnist  --asset-path outputs/sklearn_mnist_model.pkl  --experiment-name myexperiment --run-id myrunid
+  az ml model register -n sklearn_mnist  --asset-path outputs/sklearn_mnist_model.pkl  --experiment-name myexperiment --run-id myrunid --tag area=mnist
   ```
 
   [!INCLUDE [install extension](../../includes/machine-learning-service-install-extension.md)]

articles/machine-learning/how-to-deploy-existing-model.md

@@ -9,7 +9,7 @@ ms.topic: conceptual
 ms.author: jordane
 author: jpe316
 ms.reviewer: larryfr
-ms.date: 11/06/2019
+ms.date: 03/17/2020
 ---
 
 # Use an existing model with Azure Machine Learning
@@ -64,12 +64,14 @@ For more information, see the [Model.register()](https://docs.microsoft.com/pyth
 az ml model register -p ./models -n sentiment -w myworkspace -g myresourcegroup
 ```
 
+> [!TIP]
+> You can also set add `tags` and `properties` dictionary objects to the registered model. These values can be used later to help identify a specific model. For example, the framework used, training parameters, etc.
+
 For more information, see the [az ml model register](https://docs.microsoft.com/cli/azure/ext/azure-cli-ml/ml/model?view=azure-cli-latest#ext-azure-cli-ml-az-ml-model-register) reference.
 
 
 For more information on model registration in general, see [Manage, deploy, and monitor machine learning models](concept-model-management-and-deployment.md).
 
-
 ## Define inference configuration
 
 The inference configuration defines the environment used to run the deployed model. The inference configuration references the following entities, which are used to run the model when it's deployed: