Merge pull request #179829 from atikmapari/Broken-link-seramasu

Broken link fixed

Author: PRMerger15

articles/machine-learning/concept-endpoints.md

@@ -102,7 +102,7 @@ However [managed online endpoints](#managed-online-endpoints-vs-kubernetes-onlin
 
 ### Autoscaling
 
-Autoscale automatically runs the right amount of resources to handle the load on your application. Managed endpoints support autoscaling through integration with the [Azure monitor autoscale](../azure-monitor/autoscale/autoscale-overview.md) feature. You can configure metrics-based scaling (for instance, CPU utilization >70%), schedule-based scaling (for example, scaling rules for peak business hours), or a combination.
+Autoscale automatically runs the right amount of resources to handle the load on your application. Managed endpoints support autoscaling through integration with the [Azure monitor autoscale](/azure/azure-monitor/autoscale/autoscale-overview) feature. You can configure metrics-based scaling (for instance, CPU utilization >70%), schedule-based scaling (for example, scaling rules for peak business hours), or a combination.
 
 :::image type="content" source="media/concept-endpoints/concept-autoscale.png" alt-text="Screenshot showing that autoscale flexibly provides between min and max instances, depending on rules":::
 

articles/machine-learning/how-to-autoscale-endpoints.md

@@ -16,7 +16,7 @@ ms.date: 11/03/2021
 
 Autoscale automatically runs the right amount of resources to handle the load on your application. [Managed endpoints](concept-endpoints.md) supports autoscaling through integration with the Azure Monitor autoscale feature.
 
-Azure Monitor autoscaling supports a rich set of rules. You can configure metrics-based scaling (for instance, CPU utilization >70%), schedule-based scaling (for example, scaling rules for peak business hours), or a combination. For more information, see [Overview of autoscale in Microsoft Azure](/azure-monitor/autoscale/autoscale-overview.md).
+Azure Monitor autoscaling supports a rich set of rules. You can configure metrics-based scaling (for instance, CPU utilization >70%), schedule-based scaling (for example, scaling rules for peak business hours), or a combination. For more information, see [Overview of autoscale in Microsoft Azure](/azure/azure-monitor/autoscale/autoscale-overview).
 
 :::image type="content" source="media/how-to-autoscale-endpoints/concept-autoscale.png" alt-text="Diagram for autoscale adding/removing instance as needed":::
 
@@ -182,7 +182,7 @@ If you are not going to use your deployments, delete them:
 
 To learn more about autoscale with Azure Monitor, see the following articles:
 
-- [Understand autoscale settings](/azure-monitor/autoscale/autoscale-understand-settings)
-- [Overview of common autoscale patterns](/azure-monitor/autoscale/autoscale-common-scale-patterns)
-- [Best practices for autoscale](/azure-monitor/autoscale/autoscale-best-practices)
-- [Troubleshooting Azure autoscale](/azure-monitor/autoscale/autoscale-troubleshoot)
+- [Understand autoscale settings](/azure/azure-monitor/autoscale/autoscale-understanding-settings)
+- [Overview of common autoscale patterns](/azure/azure-monitor/autoscale/autoscale-common-scale-patterns)
+- [Best practices for autoscale](/azure/azure-monitor/autoscale/autoscale-best-practices)
+- [Troubleshooting Azure autoscale](/azure/azure-monitor/autoscale/autoscale-troubleshoot)

articles/machine-learning/how-to-deploy-managed-online-endpoints.md

@@ -113,7 +113,7 @@ For more information about the YAML schema, see the [online endpoint YAML refere
 > [!NOTE]
 > To use Kubernetes instead of managed endpoints as a compute target:
 > 1. Create and attach your Kubernetes cluster as a compute target to your Azure Machine Learning workspace by using [Azure Machine Learning studio](how-to-attach-arc-kubernetes.md?&tabs=studio#attach-arc-cluster).
-> 1. Use the [endpoint YAML](https://github.com/Azure/azureml-examples/blob/main/cli/endpoints/online/aks/simple-flow/1-create-aks-endpoint-with-blue.yml) to target Kubernetes instead of the managed endpoint YAML. You'll need to edit the YAML to change the value of `target` to the name of your registered compute target.
+> 1. Use the [endpoint YAML](https://github.com/Azure/azureml-examples/blob/main/cli/endpoints/online/amlarc/endpoint.yml) to target Kubernetes instead of the managed endpoint YAML. You'll need to edit the YAML to change the value of `target` to the name of your registered compute target. You can use this [deployment.yaml](azureml-examples/blue-deployment.yml at main· Azure/azureml-examples (github.com)) that has additional properties applicable to Kubernetes deployment.
 >
 > All the commands that are used in this article (except the optional SLA monitoring and Azure Log Analytics integration) can be used either with managed endpoints or with Kubernetes endpoints.