Update how-to-kubernetes-inference-routing-azureml-fe.md

azeltov · web-flow · commit f1950c9cf83d · 2022-09-07T09:37:14.000-04:00
fix spelling
diff --git a/articles/machine-learning/how-to-kubernetes-inference-routing-azureml-fe.md b/articles/machine-learning/how-to-kubernetes-inference-routing-azureml-fe.md
@@ -54,7 +54,7 @@ AzureML inference router handles autoscaling for all model deployments on the Ku
 > [!IMPORTANT]
 > * **Do not enable Kubernetes Horizontal Pod Autoscaler (HPA) for model deployments**. Doing so would cause the two auto-scaling components to compete with each other. Azureml-fe is designed to auto-scale models deployed by AzureML, where HPA would have to guess or approximate model utilization from a generic metric like CPU usage or a custom metric configuration.
 > 
-> * **Azureml-fe does not scale the nuzmber of nodes in an AKS cluster**, because this could lead to unexpected cost increases. Instead, **it scales the number of replicas for the model** within the physical cluster boundaries. If you need to scale the number of nodes within the cluster, you can manually scale the cluster or [configure the AKS cluster autoscaler](../aks/cluster-autoscaler.md).
+> * **Azureml-fe does not scale the number of nodes in an AKS cluster**, because this could lead to unexpected cost increases. Instead, **it scales the number of replicas for the model** within the physical cluster boundaries. If you need to scale the number of nodes within the cluster, you can manually scale the cluster or [configure the AKS cluster autoscaler](../aks/cluster-autoscaler.md).
 
 Autoscaling can be controlled by `scale_settings` property in deployment YAML. The following example demonstrates how to enable autoscaling:
 

Original file line number	Diff line number	Diff line change
`@@ -54,7 +54,7 @@ AzureML inference router handles autoscaling for all model deployments on the Ku`
`54`	`54`	`> [!IMPORTANT]`
`55`	`55`	`> * Do not enable Kubernetes Horizontal Pod Autoscaler (HPA) for model deployments. Doing so would cause the two auto-scaling components to compete with each other. Azureml-fe is designed to auto-scale models deployed by AzureML, where HPA would have to guess or approximate model utilization from a generic metric like CPU usage or a custom metric configuration.`
`56`	`56`	`>`
`57`		`-> * Azureml-fe does not scale the nuzmber of nodes in an AKS cluster, because this could lead to unexpected cost increases. Instead, it scales the number of replicas for the model within the physical cluster boundaries. If you need to scale the number of nodes within the cluster, you can manually scale the cluster or [configure the AKS cluster autoscaler](../aks/cluster-autoscaler.md).`
	`57`	`+> * Azureml-fe does not scale the number of nodes in an AKS cluster, because this could lead to unexpected cost increases. Instead, it scales the number of replicas for the model within the physical cluster boundaries. If you need to scale the number of nodes within the cluster, you can manually scale the cluster or [configure the AKS cluster autoscaler](../aks/cluster-autoscaler.md).`
`58`	`58`
`59`	`59`	Autoscaling can be controlled by `scale_settings` property in deployment YAML. The following example demonstrates how to enable autoscaling:
`60`	`60`