Merge branch 'patch-23' of github.com:dem108/azure-docs-pr into patch-23

msakande · msakande · commit afb3720c1bd4 · 2023-09-22T12:43:00.000-05:00
diff --git a/articles/machine-learning/how-to-deploy-online-endpoints.md b/articles/machine-learning/how-to-deploy-online-endpoints.md
@@ -73,7 +73,6 @@ Before following the steps in this article, make sure you have the following pre
 
 For managed online endpoints, Azure Machine Learning reserves 20% of your compute resources for performing upgrades on some VM SKUs. 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.  -->
 
 Azure Machine Learning provides a [shared quota](how-to-manage-quotas.md#azure-machine-learning-shared-quota) pool from which users can access quota to perform testing for a limited time. When you use the studio to deploy Llama models (from the model catalog) to a managed online endpoint, Azure Machine Learning allows you to access this shared quota for a short period. If you're deploying a _Llama-2-70b_ or _Llama-2-70b-chat_ model, you must have an [Enterprise Agreement subscription](/azure/cost-management-billing/manage/create-enterprise-subscription) before you can deploy using the shared quota. For more information on how to use the shared quota for online endpoint deployment, see [How to deploy foundation models using Studio](how-to-use-foundation-models.md#deploying-using-the-studio).
 

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`@@ -73,7 +73,6 @@ Before following the steps in this article, make sure you have the following pre`
`73`	`73`
`74`	`74`	For managed online endpoints, Azure Machine Learning reserves 20% of your compute resources for performing upgrades on some VM SKUs. 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).
`75`	`75`
`76`		-<!-- 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. -->
`77`	`76`
`78`	`77`	Azure Machine Learning provides a [shared quota](how-to-manage-quotas.md#azure-machine-learning-shared-quota) pool from which users can access quota to perform testing for a limited time. When you use the studio to deploy Llama models (from the model catalog) to a managed online endpoint, Azure Machine Learning allows you to access this shared quota for a short period. If you're deploying a _Llama-2-70b_ or _Llama-2-70b-chat_ model, you must have an [Enterprise Agreement subscription](/azure/cost-management-billing/manage/create-enterprise-subscription) before you can deploy using the shared quota. For more information on how to use the shared quota for online endpoint deployment, see [How to deploy foundation models using Studio](how-to-use-foundation-models.md#deploying-using-the-studio).
`79`	`78`