writing

Author: Larry Franks

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

@@ -733,7 +733,15 @@ for endpoint in ml_client.online_endpoints.list():
 
 # [ARM template](#tab/arm)
 
+The `show` command contains information in `provisioning_status` for endpoint and deployment:
+
+::: code language="azurecli" source="~/azureml-examples-main/cli/deploy-managed-online-endpoint.sh" ID="get_status" :::
 
+You can list all the endpoints in the workspace in a table format by using the `list` command:
+
+```azurecli
+az ml online-endpoint list --output table
+```
 
 ---
 
@@ -768,6 +776,10 @@ ml_client.online_deployments.get_logs(
 
 # [ARM template](#tab/arm)
 
+:::code language="azurecli" source="~/azureml-examples-main/cli/deploy-managed-online-endpoint.sh" ID="get_logs" :::
+
+By default, logs are pulled from inference-server. To see the logs from storage-initializer (it mounts assets like model and code to the container), add the `--container storage-initializer` flag.
+
 ---
 
 For more information on deployment logs, see [Get container logs](how-to-troubleshoot-online-endpoints.md#get-container-logs).
@@ -819,6 +831,17 @@ ml_client.online_endpoints.invoke(
 
 # [ARM template](#tab/arm)
 
+You can use either the `invoke` command or a REST client of your choice to invoke the endpoint and score some data: 
+
+::: code language="azurecli" source="~/azureml-examples-main/cli/deploy-managed-online-endpoint.sh" ID="test_endpoint" :::
+
+The following example shows how to get the key used to authenticate to the endpoint:
+
+> [!TIP]
+> You can control which Azure Active Directory security principals can get the authentication key by assigning them to a custom role that allows `Microsoft.MachineLearningServices/workspaces/onlineEndpoints/token/action` and `Microsoft.MachineLearningServices/workspaces/onlineEndpoints/listkeys/action`. For more information, see [Manage access to an Azure Machine Learning workspace](how-to-assign-roles.md).
+
+:::code language="azurecli" source="~/azureml-examples-main/cli/deploy-managed-online-endpoint.sh" ID="test_endpoint_using_curl_get_key":::
+
 ---
 
 ### (Optional) Update the deployment
@@ -921,6 +944,8 @@ ml_client.online_endpoints.begin_delete(name=online_endpoint_name)
 
 # [ARM template](#tab/arm)
 
+::: code language="azurecli" source="~/azureml-examples-main/cli/deploy-managed-online-endpoint.sh" ID="delete_endpoint" :::
+
 ---
 
 ## Next steps