Clarify on limitation for local deployment

Author: dem108

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

@@ -8,7 +8,7 @@ ms.subservice: inferencing
 author: dem108
 ms.author: sehan
 ms.reviewer: mopeakande
-ms.date: 10/18/2023
+ms.date: 11/15/2023
 reviewer: msakande
 ms.topic: how-to
 ms.custom: how-to, devplatv2, ignite-fall-2021, cliv2, event-tier1-build-2022, sdkv2
@@ -555,10 +555,11 @@ To deploy locally, [Docker Engine](https://docs.docker.com/engine/install/) must
 > [!TIP]
 > You can use [Azure Machine Learning inference HTTP server Python package](how-to-inference-server-http.md) to debug your scoring script locally **without Docker Engine**. Debugging with the inference server helps you to debug the scoring script before deploying to local endpoints so that you can debug without being affected by the deployment container configurations.
 
-Local endpoints have the following limitations:
-- They do *not* support traffic rules, authentication, or probe settings. 
-- They support only one deployment per endpoint.
-- They support local model files only. If you want to test registered models, first download them using [CLI](/cli/azure/ml/model#az-ml-model-download) or [SDK](/python/api/azure-ai-ml/azure.ai.ml.operations.modeloperations#azure-ai-ml-operations-modeloperations-download), then use `path` in the deployment definition to refer to the parent folder.
+> [!NOTE]
+> Local endpoints have the following limitations:
+> - They do *not* support traffic rules, authentication, or probe settings.
+> - They support only one deployment per endpoint.
+> - They support local model files and environment with local conda file only. If you want to test registered models, first download them using [CLI](/cli/azure/ml/model#az-ml-model-download) or [SDK](/python/api/azure-ai-ml/azure.ai.ml.operations.modeloperations#azure-ai-ml-operations-modeloperations-download), then use `path` in the deployment definition to refer to the parent folder. If you want to test registered environments, check the context of the environment in Azure Machine Learning studio and prepare local conda file to use. Example in this article demonstrates using local model and environment with local conda file, which supports local deployment. 
 
 For more information on debugging online endpoints locally before deploying to Azure, see [Debug online endpoints locally in Visual Studio Code](how-to-debug-managed-online-endpoints-visual-studio-code.md).