Merge pull request #264718 from MicrosoftDocs/repo_sync_working_branch

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

Author: Taojunshen

articles/azure-monitor/containers/container-insights-metric-alerts.md

@@ -41,14 +41,13 @@ The methods currently available for creating Prometheus alert rules are Azure Re
 
 1. Download the template that includes the set of alert rules you want to enable. For a list of the rules for each, see [Alert rule details](#alert-rule-details).
 
-   - [Community alerts](https://aka.ms/azureprometheus-communityalerts)
-   - [Recommended alerts](https://aka.ms/azureprometheus-recommendedalerts)
+   [Recommended metric alerts](https://aka.ms/azureprometheus-recommendedmetricalerts)
 
 2. Deploy the template by using any standard methods for installing ARM templates. For guidance, see [ARM template samples for Azure Monitor](../resource-manager-samples.md#deploy-the-sample-templates).
 
 ### [Bicep template](#tab/bicep)
 
-1. To deploy community and recommended alerts, follow this [template](https://aka.ms/azureprometheus-alerts-bicep) and follow the README.md file in the same folder for how to deploy.
+1. To deploy recommended metric alerts, follow this [template](https://aka.ms/azureprometheus-recommendedmetricalertsbicep) and follow the README.md file in the same folder for how to deploy.
 
 
 
@@ -111,7 +110,7 @@ The configuration change can take a few minutes to finish before it takes effect
 ### Prerequisites
 
 You might need to enable collection of custom metrics for your cluster. See [Metrics collected by Container insights](container-insights-custom-metrics.md).
-  
+
 ### Enable and configure metric alert rules
 
 #### [Azure portal](#tab/azure-portal)
@@ -181,7 +180,7 @@ The following sections present information on the alert rules provided by Contai
 
 ### Community alert rules
 
-These handpicked alerts come from the Prometheus community. Source code for these mixin alerts can be found in [GitHub](https://aka.ms/azureprometheus-communityalerts):
+These handpicked alerts come from the Prometheus community. Source code for these mixin alerts can be found in [GitHub](https://aka.ms/azureprometheus-recommendedmetricalerts):
 
 | Alert name | Description | Default threshold |
 |:---|:---|:---|
@@ -207,7 +206,7 @@ These handpicked alerts come from the Prometheus community. Source code for thes
 ### Recommended alert rules
 
 The following table lists the recommended alert rules that you can enable for either Prometheus metrics or custom metrics.
-Source code for the recommended alerts can be found in [GitHub](https://github.com/Azure/prometheus-collector/blob/68ab5b195a77d72b0b8e36e5565b645c3d1e2d5d/mixins/kubernetes/rules/recording_and_alerting_rules/templates/ci_recommended_alerts.json):
+Source code for the recommended alerts can be found in [GitHub](https://aka.ms/azureprometheus-recommendedmetricalerts):
 
 | Prometheus alert name | Custom metric alert name | Description | Default threshold |
 |:---|:---|:---|:---|

articles/azure-resource-manager/bicep/bicep-extensibility-kubernetes-provider.md

@@ -10,6 +10,21 @@ ms.date: 04/18/2023
 
 The Kubernetes provider allows you to create Kubernetes resources directly with Bicep. Bicep can deploy anything that can be deployed with the [Kubernetes command-line client (kubectl)](https://kubernetes.io/docs/reference/kubectl/kubectl/) and a [Kubernetes manifest file](../../aks/concepts-clusters-workloads.md#deployments-and-yaml-manifests).
 
+> [!NOTE]
+> Kubernetes provider is not currently supported for private clusters:
+> 
+> ```json
+> resource AKS 'Microsoft.ContainerService/managedClusters@2023-01-02-preview' = {
+>  properties: {
+>   "apiServerAccessProfile": {
+>     "enablePrivateCluster": "true"
+>     }
+>   }
+> }
+> 
+> ```
+> 
+
 ## Enable the preview feature
 
 This preview feature can be enabled by configuring the [bicepconfig.json](./bicep-config.md):

articles/machine-learning/how-to-r-modify-script-for-production.md

@@ -252,7 +252,7 @@ mlflow_log_param(<key-name>, <value>)
 
 ## Create an environment
 
-To run your R script, you'll use the `ml` extension for Azure CLI, also referred to as CLI v2. The `ml` command uses a YAML job definitions file. For more information about submitting jobs with `az ml`, see [Train models with Azure Machine Learning CLI](how-to-train-model.md?tabs=azurecli#4-submit-the-training-job).
+To run your R script, you'll use the `ml` extension for Azure CLI, also referred to as CLI v2. The `ml` command uses a YAML job definitions file. For more information about submitting jobs with `az ml`, see [Train models with Azure Machine Learning CLI](how-to-train-model.md?tabs=azurecli#3-submit-the-training-job).
 
 The YAML job file specifies an [environment](concept-environments.md). You'll need to create this environment in your workspace before you can run the job.  
 

articles/machine-learning/how-to-train-model.md

@@ -136,7 +136,7 @@ When you train using the REST API, data and training scripts must be uploaded to
 ### 2. Create a compute resource for training
 
 > [!NOTE]
-> To try [serverless compute](./how-to-use-serverless-compute.md), skip this step and proceed to [ 4. Submit the training job](#4-submit-the-training-job).
+> To try [serverless compute](./how-to-use-serverless-compute.md), skip this step and proceed to [ 3. Submit the training job](#3-submit-the-training-job).
 
 An Azure Machine Learning compute cluster is a fully managed compute resource that can be used to run the training job. In the following examples, a compute cluster named `cpu-compute` is created.
 
@@ -181,7 +181,7 @@ curl -X PUT \
 
 ---
 
-### 4. Submit the training job
+### 3. Submit the training job
 
 # [Python SDK](#tab/python)