Merge pull request #551 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-setup-mlops-github-azure-ml.md

@@ -44,9 +44,9 @@ In this article, you learn about using Azure Machine Learning to set up an end-t
 > [!IMPORTANT]
 >The CLI commands in this article were tested using Bash. If you use a different shell, you may encounter errors.
 
-## Set up authentication with Azure and DevOps
+## Set up authentication with Azure and GitHub Actions
 
-Before you can set up an MLOps project with Machine Learning, you need to set up authentication for Azure DevOps.
+Before you can set up an MLOps project with Machine Learning, you need to set up authentication for GitHub Actions.
 
 ### Create service principal
    Create one Prod service principal for this demo. You can add more depending on how many environments, you want to work on (Dev or Prod or Both). Service principals can be created using one of the following methods:

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

@@ -257,7 +257,6 @@ volcano-scheduler.conf: |
     - plugins:
       - name: conformance
     - plugins:
-        - name: overcommit
         - name: drf
         - name: predicates
         - name: proportion
@@ -269,13 +268,15 @@ To use this config in your AKS cluster, you need to follow the following steps:
 1. Create a configmap file with the above config in the `azureml` namespace. This namespace will generally be created when you install the Azure Machine Learning extension.
 1. Set `volcanoScheduler.schedulerConfigMap=<configmap name>` in the extension config to apply this configmap. And you need to skip the resource validation when installing the extension by configuring `amloperator.skipResourceValidation=true`. For example:
     ```azurecli
-    az k8s-extension update --name <extension-name> --extension-type Microsoft.AzureML.Kubernetes --config volcanoScheduler.schedulerConfigMap=<configmap name> amloperator.skipResourceValidation=true --cluster-type managedClusters --cluster-name <your-AKS-cluster-name> --resource-group <your-RG-name> --scope cluster
+    az k8s-extension update --name <extension-name> --config volcanoScheduler.schedulerConfigMap=<configmap name> amloperator.skipResourceValidation=true --cluster-type managedClusters --cluster-name <your-AKS-cluster-name> --resource-group <your-RG-name>
     ```
 
 > [!NOTE]
 > Since the gang plugin is removed, there's potential that the deadlock happens when volcano schedules the job. 
 > 
 > * To avoid this situation, you can **use same instance type across the jobs**.
+> 
+> Using a scheduler configuration other than the default provided by the Azure Machine Learning extension may not be fully supported. Proceed with caution.
 >
 > Note that you need to disable `job/validate` webhook in the volcano admission if your **volcano version is lower than 1.6**.
 

articles/search/search-sku-tier.md

@@ -60,6 +60,7 @@ Currently, several regions are at capacity for specific tiers and can't be used
 | Region | Disabled tier (SKU) due to over-capacity |
 |--------|------------------------------------------|
 | Central US | S2, S3, S3HD, L1, L2 |
+| East US| All tiers|
 | Japan East | S2, S3, S3HD, L1, L2 |
 | Qatar Central | All tiers|
 | South Central US | All tiers |