Merge pull request #3294 from MicrosoftDocs/repo_sync_working_branch

Confirm merge from repo_sync_working_branch to main to sync with https://github.com/MicrosoftDocs/azure-ai-docs (branch main)

Author: Taojunshen

articles/machine-learning/how-to-deploy-kubernetes-extension.md

@@ -83,8 +83,8 @@ If you plan to deploy Azure Machine Learning extension for real-time inference w
 
   * `azureml-fe` router service is required for real-time inference support and you need to specify `inferenceRouterServiceType` config setting for `azureml-fe`. `azureml-fe` can be deployed with one of following `inferenceRouterServiceType`:
       * Type `LoadBalancer`. Exposes `azureml-fe` externally using a cloud provider's load balancer. To specify this value, ensure that your cluster supports load balancer provisioning. Note most on-premises Kubernetes clusters might not support external load balancer.
-      * Type `NodePort`. Exposes `azureml-fe` on each Node's IP at a static port. You'll be able to contact `azureml-fe`, from outside of cluster, by requesting `<NodeIP>:<NodePort>`. Using `NodePort` also allows you to set up your own load balancing solution and TLS/SSL termination for `azureml-fe`.
-      * Type `ClusterIP`. Exposes `azureml-fe` on a cluster-internal IP, and it makes `azureml-fe` only reachable from within the cluster. For `azureml-fe` to serve inference requests coming outside of cluster, it requires you to set up your own load balancing solution and TLS/SSL termination for `azureml-fe`. 
+      * Type `NodePort`. Exposes `azureml-fe` on each Node's IP at a static port. You'll be able to contact `azureml-fe`, from outside of cluster, by requesting `<NodeIP>:<NodePort>`. Using `NodePort` also allows you to set up your own load balancing solution and TLS/SSL termination for `azureml-fe`. For more details on how to set up your own ingress, see [Integrate other ingress controller with Azure Machine Learning extension over HTTP or HTTPS](./reference-kubernetes.md#integrate-other-ingress-controller-with-azure-machine-learning-extension-over-http-or-https).
+      * Type `ClusterIP`. Exposes `azureml-fe` on a cluster-internal IP, and it makes `azureml-fe` only reachable from within the cluster. For `azureml-fe` to serve inference requests coming outside of cluster, it requires you to set up your own load balancing solution and TLS/SSL termination for `azureml-fe`. For more details on how to set up your own ingress, see [Integrate other ingress controller with Azure Machine Learning extension over HTTP or HTTPS](./reference-kubernetes.md#integrate-other-ingress-controller-with-azure-machine-learning-extension-over-http-or-https).
    * To ensure high availability of `azureml-fe` routing service, Azure Machine Learning extension deployment by default creates three replicas of `azureml-fe` for clusters having three nodes or more. If your cluster has **less than 3 nodes**, set `inferenceRouterHA=False`.
    * You also want to consider using **HTTPS** to restrict access to model endpoints and secure the data that clients submit. For this purpose, you would need to specify either `sslSecret` config setting or combination of `sslKeyPemFile` and `sslCertPemFile` config-protected settings. 
    * By default, Azure Machine Learning extension deployment expects config settings for **HTTPS** support. For development or testing purposes, **HTTP** support is conveniently provided through config setting `allowInsecureConnections=True`.