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Copy file name to clipboardExpand all lines: articles/ai-studio/includes/create-hub.md
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@@ -26,7 +26,7 @@ To create a hub in [Azure AI Studio](https://ai.azure.com), follow these steps:
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:::image type="content" source="../media/how-to/hubs/hub-new-connect-services.png" alt-text="Screenshot of the dialog to connect services while creating a new hub." lightbox="../media/how-to/hubs/hub-new-connect-services.png":::
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> [!NOTE]
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> If you don't see (new) before the **Resource group** and **Connect Azure AI Services** entries then an existing resource is being used. For the purposes of this tutorial, create a seperate entity via **Create new resource group** and **Create new AI Services**. This will allow you to prevent any unexpected charges by deleting the entities after the tutorial.
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> If you don't see (new) before the **Resource group** and **Connect Azure AI Services** entries then an existing resource is being used. For the purposes of this tutorial, create a separate entity via **Create new resource group** and **Create new AI Services**. This will allow you to prevent any unexpected charges by deleting the entities after the tutorial.
Copy file name to clipboardExpand all lines: articles/machine-learning/reference-managed-online-endpoints-vm-sku-list.md
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* For more information on configuration details such as CPU and RAM, see [Azure Machine Learning Pricing](https://azure.microsoft.com/pricing/details/machine-learning/) and [VM sizes](/azure/virtual-machines/sizes).
> `Standard_DS1_v2` and `Standard_F2s_v2` may be too small for bigger models and may lead to container termination due to insufficient memory, not enough space on the disk, or probe failure as it takes too long to initiate the container. If you face [OutOfQuota errors](how-to-troubleshoot-online-endpoints.md?tabs=cli#error-outofquota) or [ReourceNotReady errors](how-to-troubleshoot-online-endpoints.md?tabs=cli#error-resourcenotready), try bigger VM SKUs. If you want to reduce the cost of deploying multiple models with managed online endpoint, see [Deployment for several local models](concept-online-deployment-model-specification.md#deployment-for-several-local-models).
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> Small VM SKUs such as `Standard_DS1_v2` and `Standard_F2s_v2` may be too small for bigger models and may lead to container termination due to insufficient memory, not enough space on the disk, or probe failure as it takes too long to initiate the container. If you face [OutOfQuota errors](how-to-troubleshoot-online-endpoints.md?tabs=cli#error-outofquota) or [ReourceNotReady errors](how-to-troubleshoot-online-endpoints.md?tabs=cli#error-resourcenotready), try bigger VM SKUs. If you want to reduce the cost of deploying multiple models with managed online endpoint, see [Deployment for several local models](concept-online-deployment-model-specification.md#deployment-for-several-local-models).
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> [!NOTE]
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> We recommend having more than 3 instances for deployments in production scenarios. In addition, Azure Machine Learning reserves 20% of your compute resources for performing upgrades on some VM SKUs as described in [Virtual machine quota allocation for deployment](how-to-manage-quotas.md#virtual-machine-quota-allocation-for-deployment). VM SKUs that are exempted from this extra quota reservation are listed below:
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> - Standard_NC24ads_A100_v4
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> - Standard_NC48ads_A100_v4
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> - Standard_NC96ads_A100_v4
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> - Standard_ND96asr_v4
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> - Standard_ND96amsr_A100_v4
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> - Standard_ND40rs_v2
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> We recommend having more than 3 instances for deployments in production scenarios. In addition, Azure Machine Learning reserves 20% of your compute resources for performing upgrades on some VM SKUs as described in [Virtual machine quota allocation for deployment](how-to-manage-quotas.md#virtual-machine-quota-allocation-for-deployment). VM SKUs that are exempted from this extra quota reservation are specified in the "Skip 20% Reservation" column.
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