Merge pull request #233356 from dem108/patch-20

fix VM SKUs

Author: prmerger-automator[bot]

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

@@ -72,7 +72,7 @@ Before following the steps in this article, make sure you have the following pre
 
 ### Virtual machine quota allocation for deployment
 
-For managed online endpoints, Azure Machine Learning reserves 20% of your compute resources for performing upgrades. Therefore, if you request a given number of instances in a deployment, you must have a quota for `ceil(1.2*number of instances requested for deployment)* number of cores for the VM SKU` available to avoid getting an error. For example, if you request 10 instances of a [Standard_DS2_v2](/azure/virtual-machines/dv2-dsv2-series) VM (that comes with 2 cores) in a deployment, you should have a quota for 24 cores (`12 instances*2 cores`) available. To view your usage and request quota increases, see [View your usage and quotas in the Azure portal](how-to-manage-quotas.md#view-your-usage-and-quotas-in-the-azure-portal).
+For managed online endpoints, Azure Machine Learning reserves 20% of your compute resources for performing upgrades. Therefore, if you request a given number of instances in a deployment, you must have a quota for `ceil(1.2 * number of instances requested for deployment) * number of cores for the VM SKU` available to avoid getting an error. For example, if you request 10 instances of a [Standard_DS3_v2](/azure/virtual-machines/dv2-dsv2-series) VM (that comes with 4 cores) in a deployment, you should have a quota for 48 cores (`12 instances * 4 cores`) available. To view your usage and request quota increases, see [View your usage and quotas in the Azure portal](how-to-manage-quotas.md#view-your-usage-and-quotas-in-the-azure-portal).
 
 <!-- In this tutorial, you'll request one instance of a Standard_DS2_v2 VM SKU (that comes with 2 cores) in your deployment; therefore, you should have a minimum quota for 4 cores (`2 instances*2 cores`) available.  -->
 ---
@@ -461,7 +461,7 @@ For information on creating an environment in the studio, see [Create an environ
 
 # [Azure CLI](#tab/azure-cli)
 
-The preceding definition in the _blue-deployment.yml_ file uses a general-purpose type `Standard_DS2_v2` instance and a non-GPU Docker image `mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04:latest`. For GPU compute, choose a GPU compute type SKU and a GPU Docker image.
+The preceding definition in the _blue-deployment.yml_ file uses a general-purpose type `Standard_DS3_v2` instance and a non-GPU Docker image `mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04:latest`. For GPU compute, choose a GPU compute type SKU and a GPU Docker image.
 
 For supported general-purpose and GPU instance types, see [Managed online endpoints supported VM SKUs](reference-managed-online-endpoints-vm-sku-list.md). For a list of Azure Machine Learning CPU and GPU base images, see [Azure Machine Learning base images](https://github.com/Azure/AzureML-Containers).
 
@@ -470,7 +470,7 @@ For supported general-purpose and GPU instance types, see [Managed online endpoi
 
 # [Python](#tab/python)
 
-The preceding definition of the `blue_deployment` uses a general-purpose type `Standard_DS2_v2` instance and a non-GPU Docker image `mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04:latest`. For GPU compute, choose a GPU compute type SKU and a GPU Docker image.
+The preceding definition of the `blue_deployment` uses a general-purpose type `Standard_DS3_v2` instance and a non-GPU Docker image `mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04:latest`. For GPU compute, choose a GPU compute type SKU and a GPU Docker image.
 
 For supported general-purpose and GPU instance types, see [Managed online endpoints supported VM SKUs](reference-managed-online-endpoints-vm-sku-list.md). For a list of Azure Machine Learning CPU and GPU base images, see [Azure Machine Learning base images](https://github.com/Azure/AzureML-Containers).
 
@@ -1178,4 +1178,4 @@ If you aren't going use the deployment, you should delete it by running the foll
 - [Enable network isolation with managed online endpoints](how-to-secure-online-endpoint.md)
 - [View costs for an Azure Machine Learning managed online endpoint](how-to-view-online-endpoints-costs.md)
 - [Manage and increase quotas for resources with Azure Machine Learning](how-to-manage-quotas.md#azure-machine-learning-managed-online-endpoints)
-- [Use batch endpoints for batch scoring](batch-inference/how-to-use-batch-endpoint.md)
+- [Use batch endpoints for batch scoring](batch-inference/how-to-use-batch-endpoint.md)