Update k8s compute TSG and log info

Author: jiaochenlu

articles/machine-learning/how-to-troubleshoot-kubernetes-compute.md

@@ -173,7 +173,7 @@ Cannot found Kubernetes cluster.
 This error should occur when the system cannot find the AKS/Arc-Kubernetes cluster.
 
 You can check the following items to troubleshoot the issue:
-* First, check the cluster resource ID in the Azure Portal to verify whether Kubernetes cluster resource still exists and is running normally.
+* First, check the cluster resource ID in the Azure portal to verify whether Kubernetes cluster resource still exists and is running normally.
 * If the cluster exists and is running, then you can try to detach and reattach the compute to the workspace. Pay attention to more notes on [reattach](#error-genericcomputeerror).
 
 > [!TIP]

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

@@ -450,7 +450,7 @@ To run the `score.py` provided as part of the deployment, Azure creates a contai
     - A failure in the `init()` method.
 - If `get-logs` isn't producing any logs, it usually means that the container has failed to start. To debug this issue, try [deploying locally](#deploy-locally) instead.
 - Readiness or liveness probes aren't set up correctly.
-- There's an error in the environment setup of the container, such as a missing dependency.
+- There's an error in the environment set up of the container, such as a missing dependency.
 - When you face `TypeError: register() takes 3 positional arguments but 4 were given` error, the error may be caused by the dependency between flask v2 and `azureml-inference-server-http`. See [FAQs for inference HTTP server](how-to-inference-server-http.md#1-i-encountered-the-following-error-during-server-startup) for more details.
 
 ### ERROR: ResourceNotFound
@@ -524,7 +524,7 @@ Below is a list of reasons you might run into this error when creating/updating
 * Role assignment has not yet been completed. In this case, please wait for a few seconds and try again later. 
 * The Azure ARC (For Azure Arc Kubernetes cluster) or Azure Machine Learning extension (For AKS) is not properly installed or configured. Please try to check the Azure ARC or Azure Machine Learning extension configuration and status. 
 * The Kubernetes cluster has improper network configuration, please check the proxy, network policy or certificate.
-  * If you are using a private AKS cluster, it is necessary to setup private endpoints for ACR, storage account, workspace in the AKS vnet. 
+  * If you are using a private AKS cluster, it is necessary to set up private endpoints for ACR, storage account, workspace in the AKS vnet. 
 
 ### ERROR: EndpointNotFound
 

articles/machine-learning/reference-kubernetes.md

@@ -112,7 +112,7 @@ Some logs about AzureML workloads in the cluster will be collected through exten
 |amlarc-identity-controller	|Request and renew Azure Blob/Azure Container Registry token through managed identity.	|Only used when `enableInference=true` is set when installing the extension. It has trace logs for status on getting identity for endpoints to authenticate with AzureML service.|
 |amlarc-identity-proxy	|Request and renew Azure Blob/Azure Container Registry token through managed identity.	|Only used when `enableInference=true` is set when installing the extension. It has trace logs for status on getting identity for the cluster to authenticate with AzureML service.|
 |aml-operator	| Manage the lifecycle of training jobs.	|The logs contain AzureML training job pod status in the cluster.|
-|azureml-fe-v2|	The front-end component that routes incoming inference requests to deployed services.	|Access logs at request level, including request Id, start time, response code, error details and durations for request latency. Trace logs for service metadata changes, service running healthy status, etc. for debugging purpose.|
+|azureml-fe-v2|	The front-end component that routes incoming inference requests to deployed services.	|Access logs at request level, including request ID, start time, response code, error details and durations for request latency. Trace logs for service metadata changes, service running healthy status, etc. for debugging purpose.|
 | gateway	| The gateway is used to communicate and send data back and forth.	| Trace logs on requests from AzureML services to the clusters.|
 |healthcheck	|--| 	The logs contain azureml namespace resource (AzureML extension) status to diagnose what make the extension not functional. |
 |inference-operator-controller-manager|	Manage the lifecycle of inference endpoints.	|The logs contain AzureML inference endpoint and deployment pod status in the cluster.|