incorporating feedback

Author: Larry

articles/machine-learning/concept-model-management-and-deployment.md

@@ -151,7 +151,7 @@ Azure ML gives you the capability to track the end-to-end audit trail of all of
 - [Integration with Azure Event Grid](concept-event-grid-integration.md) allows you to act on events in the ML lifecycle. For example, model registration, deployment, data drift, and training (run) events.
 
 > [!TIP]
-> While some information on models and datasets are automatically captured, you can add additional information by using __tags__. When looking for registered models and datasets in your workspace, you can use tags as a filter.
+> While some information on models and datasets is automatically captured, you can add additional information by using __tags__. When looking for registered models and datasets in your workspace, you can use tags as a filter.
 >
 > Associating a dataset with a registered model is an optional step. For information on referencing a dataset when registering a model, see the [Model](https://docs.microsoft.com/python/api/azureml-core/azureml.core.model(class)?view=azure-ml-py) class reference.