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

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@@ -71,7 +71,7 @@ Before following the steps in this article, make sure you have the following pre
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### Virtual machine quota allocation for deployment
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For managed online endpoints, Azure Machine Learning reserves 20% of your compute resources for performing upgrades on selected VM SKUs. 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).
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For managed online endpoints, Azure Machine Learning reserves 20% of your compute resources for performing upgrades on some VM SKUs. 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).
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<!-- 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. -->
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