Merge pull request #122874 from Martin-Jia/mingj/fix-aks-v1-logging

Combine callouts and refine the doc

Author: Court72

articles/machine-learning/v1/how-to-deploy-azure-kubernetes-service.md

@@ -88,13 +88,9 @@ In Azure Machine Learning, deployment is used in the more general sense of makin
 The front-end component (azureml-fe) that routes incoming inference requests to deployed services automatically scales as needed. Scaling of azureml-fe is based on the AKS cluster purpose and size (number of nodes). The cluster purpose and nodes are configured when you [create or attach an AKS cluster](../how-to-create-attach-kubernetes.md). There's one azureml-fe service per cluster, which might be running on multiple pods.
 
 > [!IMPORTANT]
-> When using a cluster configured as `dev-test`, the self-scaler is *disabled*. Even for FastProd/DenseProd clusters, Self-Scaler is only enabled when telemetry shows that it's needed.
-
-> [!IMPORTANT]
-> Azure Machine Learning does not upload and or save container logs, even for system containers. For full debuggability, you should [enable Container Insights for your AKS cluster](../../azure-monitor/containers/kubernetes-monitoring-enable.md#enable-container-insights) on your own to save and manage container logs, and share the related logs with AML team when necessary. Otherwise, AML does not commit SLA on azureml-fe related issues.
-
-> [!NOTE]
-> The maximum request payload is 100MB.
+> * When using a cluster configured as `dev-test`, the self-scaler is *disabled*. Even for FastProd/DenseProd clusters, Self-Scaler is only enabled when telemetry shows that it's needed.
+> * Azure Machine Learning doesn't automatically upload or store logs from any containers, including system containers. For comprehensive debugging, it's recommended that you [enable Container Insights for your AKS cluster](../../azure-monitor/containers/kubernetes-monitoring-enable.md#enable-container-insights). This allows you to save, manage, and share container logs with the AML team when needed. Without this, AML can't guarantee support for issues related to azureml-fe.
+> * The maximum request payload is 100MB.
 
 Azureml-fe scales both up (vertically) to use more cores, and out (horizontally) to use more pods. When making the decision to scale up, the time that it takes to route incoming inference requests is used. If this time exceeds the threshold, a scale-up occurs. If the time to route incoming requests continues to exceed the threshold, a scale-out occurs.