Merge pull request #180443 from lobrien/patch-sethu-moe-mentions

Emphasizing managed online endpoints per PM

Author: PRMerger8

articles/machine-learning/concept-endpoints.md

@@ -114,7 +114,9 @@ Visual Studio Code enables you to interactively debug endpoints.
 
 ## Managed online endpoints vs Kubernetes online endpoints (preview)
 
-There are two types of online endpoints: **managed online endpoints** (preview) and **Kubernetes online endpoints** (preview). The following table highlights some of their key differences.
+There are two types of online endpoints: **managed online endpoints** (preview) and **Kubernetes online endpoints** (preview). Managed online endpoints help to deploy your ML models in a turnkey manner. Managed online endpoints work with powerful CPU and GPU machines in Azure in a scalable, fully managed way. Managed online endpoints take care of serving, scaling, securing, and monitoring your models, freeing you from the overhead of setting up and managing the underlying infrastructure. The main example in this doc uses managed online endpoints for deployment. 
+
+The following table highlights the key differences between managed online endpoints and Kubernetes online endpoints. 
 
 |  | Managed online endpoints | Kubernetes online endpoints |
 |-|-|-|

articles/machine-learning/how-to-deploy-managed-online-endpoints.md

@@ -19,7 +19,7 @@ Learn how to use an online endpoint (preview) to deploy your model, so you don't
 
 You'll also learn how to view the logs and monitor the service-level agreement (SLA). You start with a model and end up with a scalable HTTPS/REST endpoint that you can use for online and real-time scoring. 
 
-For more information, see [What are Azure Machine Learning endpoints (preview)?](concept-endpoints.md).
+Managed online endpoints help to deploy your ML models in a turnkey manner. Managed online endpoints work with powerful CPU and GPU machines in Azure in a scalable, fully managed way. Managed online endpoints take care of serving, scaling, securing, and monitoring your models, freeing you from the overhead of setting up and managing the underlying infrastructure. The main example in this doc uses managed online endpoints for deployment. To use Kubernetes instead, see the notes in this document inline with the managed online endpoint discussion. For more information, see [What are Azure Machine Learning endpoints (preview)?](concept-endpoints.md).
 
 [!INCLUDE [preview disclaimer](../../includes/machine-learning-preview-generic-disclaimer.md)]