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‎.openpublishing.redirection.json

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{
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"source_path": "articles/genomics/frequently-asked-questions-genomics.yml",
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"redirect_document_id": false
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"redirect_url": "/previous-versions/azure/genomics/quickstart-input-pair-fastq",
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{
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"source_path": "articles/genomics/quickstart-run-genomics-workflow-portal.md",
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"redirect_url": "/previous-versions/azure/genomics/quickstart-run-genomics-workflow-portal",
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},
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"source_path": "articles/genomics/troubleshooting-guide-genomics.md",
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"redirect_url": "/previous-versions/azure/genomics/troubleshooting-guide-genomics",
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"redirect_document_id": false
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},
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{
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"source_path": "articles/genomics/version-release-history-genomics.md",
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"redirect_url": "/previous-versions/azure/genomics/version-release-history-genomics",
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"redirect_document_id": false
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},
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{
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"source_path_from_root": "/articles/ai-studio/concepts/what-are-ai-services.md",
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"redirect_url": "/azure/ai-services/what-are-ai-services",
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"redirect_document_id": false
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},
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{
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"source_path_from_root": "/articles/ai-services/openai/concepts/use-your-image-data.md",
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"redirect_url": "/azure/ai-services/openai/concepts/use-your-data",
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"source_path_from_root": "/articles/ai-services/openai/references/azure-machine-learning.md",
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"redirect_url": "/azure/ai-services/openai/concepts/use-your-data",
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},
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{
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"source_path_from_root": "/articles/open-datasets/dataset-covid-19-open-research.md",
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{
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"source_path_from_root": "/articles/open-datasets/dataset-genomics-data-lake.md",
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"redirect_url": "/azure/open-datasets/dataset-catalog",
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"redirect_document_id": false
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}
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]
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}
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}

‎.vscode/settings.json

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{
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"cSpell.words": [
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"DALL"
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]
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}

‎.whatsnew.json

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{
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"$schema": "https://whatsnewapi.azurewebsites.net/schema",
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"docSetProductName": "Azure AI Services",
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"rootDirectory": "articles/",
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"docLinkSettings": {
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"linkFormat": "relative",
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"relativeLinkPrefix": "../"
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},
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"navigationOptions": {
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"maximumNumberOfArticles": 5,
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"tocParentNode": "Latest documentation updates",
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"repoTocFolder": "articles/ai-services",
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"indexParentNode": "Latest documentation updates",
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"repoIndexFolder": "articles/ai-services"
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},
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"areas": [
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{
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"names": [ "ai-services" ],
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"heading": "Azure AI Services"
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},
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{
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"names": [ "ai-studio" ],
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"heading": "Azure AI Foundry"
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},
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{
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"names": [ "machine-learning" ],
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"heading": "Azure Machine Learning"
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},
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{
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"names": [ "search" ],
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"heading": "Azure AI Search"
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}
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]
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}

‎articles/ai-foundry/model-inference/how-to/configure-deployment-policies.md

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---
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title: Control model deployment with custom policies
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titleSuffix: Azure AI Foundry
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description: "Learn how to use custom Azure Policies to control Azure AI services and Azure OpenAI model deployment with Azure AI Foundry."
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manager: scottpolly
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ms.service: azure-ai-foundry
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ms.topic: how-to #Don't change
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ms.date: 02/11/2025
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author: Blackmist
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ms.author: larryfr
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reviewer: santiagxf
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ms.reviewer: fasantia
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---
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# Control model deployment with custom policies
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When using models from Azure AI Services and Azure OpenAI with [Azure AI Foundry](https://ai.azure.com), you might need to use custom policies to control which [type of deployment](../concepts/deployment-types.md) options are available to users or which specific models users can deploy. This article guides you on how to create policies to control model deployments using Azure Policies.
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## Prerequisites
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- An Azure subscription. If you don't have an Azure subscription, create a [free account](https://azure.microsoft.com/free/) before you begin.
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- Permissions to create and assign policies. To create and assign policies, you must be an [Owner](/azure/role-based-access-control/built-in-roles#owner) or [Resource Policy Contributor](/azure/role-based-access-control/built-in-roles#resource-policy-contributor) at the Azure subscription or resource group level.
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- Familiarity with Azure Policy. To learn more, see [What is Azure Policy?](/azure/governance/policy/overview).
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## Create a custom policy
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Follow these steps to create and assign an example custom policy to control model deployments:
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1. From the [Azure portal](https://portal.azure.com), select **Policy** from the left side of the page. You can also search for **Policy** in the search bar at the top of the page.
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2. From the left side of the Azure Policy Dashboard, select **Authoring**, **Definitions**, and then select **+ Policy definition** from the top of the page.
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:::image type="content" source="../media/configure-deployment-policies/create-new-policy.png" alt-text="An screenshot showing how to create a new policy definition in Azure Policies." lightbox="../media/configure-deployment-policies/create-new-policy.png":::
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3. In the **Policy Definition** form, use the following values:
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- **Definition location**: Select the subscription or management group where you want to store the policy definition.
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- **Name**: Enter a unique name for the policy definition. For example, `Custom allowed Azure AI services and Azure OpenAI models`.
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- **Description**: Enter a description for the policy definition.
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- **Category**: You can either create a new category or use an existing one. For example, "AI model governance."
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4. On **Policy rule**, enter the policy rule details in JSON format. Select the scenario that applies to your case better:
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# [Enforce specific models](#tab/models)
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The following policy allows you to control which specific models and versions are available for deployment. You can enforce this policy at different levels depending on your needs.
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```json
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{
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"mode": "All",
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"policyRule": {
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"if": {
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"allOf": [
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{
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"field": "type",
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"equals": "Microsoft.CognitiveServices/accounts/deployments"
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},
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{
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"not": {
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"value": "[concat(field('Microsoft.CognitiveServices/accounts/deployments/model.name'), ',', field('Microsoft.CognitiveServices/accounts/deployments/model.version'))]",
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"in": "[parameters('allowedModels')]"
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}
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}
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]
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},
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"then": {
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"effect": "deny"
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}
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},
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"parameters": {
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"allowedModels": {
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"type": "Array",
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"metadata": {
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"displayName": "Allowed AI models",
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"description": "The list of allowed models to be deployed."
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}
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}
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}
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}
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```
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# [Enforce specific deployment types](#tab/deployments)
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The following policy allows you to control which types of deployments are allowed in the Azure AI Services or Azure OpenAI Resources. For example, you might want to prevent developers from creating deployments that result in data processed in a different region. Follow these steps to create a policy that denies creating global processing deployment types.
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```json
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{
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"mode": "All",
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"policyRule": {
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"if": {
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"allOf": [
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{
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"field": "type",
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"equals": "Microsoft.CognitiveServices/accounts/deployments"
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},
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{
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"field": "Microsoft.CognitiveServices/accounts/deployments/sku.name",
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"equals": "GlobalStandard"
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}
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]
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},
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"then": {
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"effect": "deny"
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}
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}
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}
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```
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---
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> [!TIP]
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> Azure AI services was originally named Azure Cognitive Services. This name is still used internally by Azure, such as this custom policy where you see a value of `Microsoft.CognitiveServices`. Azure OpenAI is part of Azure AI services, so this policy also applies to Azure OpenAI models.
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5. Select **Save** to save the policy definition. After saving, you arrive at the policy definition's overview page.
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6. From the policy definition's overview page, select **Assign policy** to assign the policy definition.
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7. From the **Assign policy** page, use the following values on the **Basics** tab:
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- **Scope**: Select the scope where you want to assign the policy. The scope can be a management group, subscription, or resource group.
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- **Policy definition**: This field is prepopulated with the title of policy definition you created previously.
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- **Assignment name**: Enter a unique name for the assignment.
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- **Policy enforcement**: Make sure that the **Policy enforcement** field is set to **Enabled**. If it isn't enabled, the policy isn't enforced.
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Select **Next** at the bottom of the page, or the **Parameters** tab at the top of the page.
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8. Configure the parameters for the policy (if any):
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# [Enforce specific models](#tab/models)
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From the **Parameters** tab, set **Allowed AI models** to the list of models that you want to allow. The list should be a comma-separated list of model names and approved versions, surrounded by square brackets. For example, `["gpt-4,0613", "gpt-35-turbo,0613"]`.
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> [!TIP]
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> You can find the model names and their versions in the [Azure AI Foundry Model Catalog](https://ai.azure.com/explore/models). Select the model to view the details, and then copy the model name and their version in the title.
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# [Enforce specific deployment types](#tab/deployments)
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This policy doesn't require parameters.
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9. Optionally, select the **Non-compliance messages** tab at the top of the page and set a custom message for noncompliance.
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10. Select **Review + create** tab and verify that the policy assignment is correct. When ready, select **Create** to assign the policy.
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11. Notify your developers that the policy is in place. They receive an error message if they try to deploy a model that isn't in the list of allowed models.
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## Verify policy assignment
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To verify that the policy is assigned, navigate to **Policy** in the Azure portal, and then select **Assignments** under **Authoring**. You should see the policy listed.
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## Monitor compliance
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To monitor compliance with the policy, follow these steps:
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1. From the [Azure portal](https://portal.azure.com), select **Policy** from the left side of the page. You can also search for **Policy** in the search bar at the top of the page.
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1. From the left side of the Azure Policy Dashboard, select **Compliance**. Each policy assignment is listed with the compliance status. To view more details, select the policy assignment. The following example shows the compliance report for a policy that blocks deployments of type *Global standard*.
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:::image type="content" source="../media/configure-deployment-policies/policy-compliance.png" alt-text="An screenshot showing an example of a policy compliance report for a policy that blocks Global standard deployment SKUs." lightbox="../media/configure-deployment-policies/policy-compliance.png":::
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## Update the policy assignment
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To update an existing policy assignment with new models, follow these steps:
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1. From the [Azure portal](https://portal.azure.com), select **Policy** from the left side of the page. You can also search for **Policy** in the search bar at the top of the page.
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1. From the left side of the Azure Policy Dashboard, select **Assignments** and find the existing policy assignment. Select the ellipsis (...) next to the assignment and select **Edit assignment**.
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1. From the **Parameters** tab, update the **Allowed models** parameter with the new models.
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1. From the **Review + Save** tab, select **Save** to update the policy assignment.
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## Best practices
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- **Granular scoping**: Assign policies at the appropriate scope to balance control and flexibility. For example, apply at the subscription level to control all resources in the subscription, or apply at the resource group level to control resources in a specific group.
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- **Policy naming**: Use a consistent naming convention for policy assignments to make it easier to identify the purpose of the policy. Include information such as the purpose and scope in the name.
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- **Documentation**: Keep records of policy assignments and configurations for auditing purposes. Document any changes made to the policy over time.
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- **Regular reviews**: Periodically review policy assignments to ensure they align with your organization's requirements.
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- **Testing**: Test policies in a nonproduction environment before applying them to production resources.
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- **Communication**: Make sure developers are aware of the policies in place and understand the implications for their work.
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## Related content
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- [Azure Policy overview](/azure/governance/policy/overview)
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- [Deployment types](../concepts/deployment-types.md)
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‎articles/ai-foundry/model-inference/how-to/quickstart-create-resources.md

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::: zone pivot="programming-language-bicep"
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[!INCLUDE [bicep](../includes/create-resources/bicep.md)]
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::: zone-end
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## Next steps
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* [Develop applications using Azure AI model inference service in Azure AI services](../supported-languages.md)

‎articles/ai-foundry/model-inference/how-to/use-chat-completions.md

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## Related content
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* [Use embeddings models](use-embeddings.md)
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* [Use image embeddings models](use-image-embeddings.md)
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* [Use reasoning models](use-chat-reasoning.md)
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* [Azure AI Model Inference API](.././reference/reference-model-inference-api.md)

‎articles/ai-foundry/model-inference/how-to/use-embeddings.md

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## Related content
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* [Use embeddings models](use-embeddings.md)
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* [Use image embeddings models](use-image-embeddings.md)
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* [Azure AI Model Inference API](.././reference/reference-model-inference-api.md)

‎articles/ai-foundry/model-inference/includes/code-create-chat-client-entra.md

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endpoint="https://<resource>.services.ai.azure.com/models",
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credential=DefaultAzureCredential(),
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credential_scopes=["https://cognitiveservices.azure.com/.default"],
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model="mistral-large-2407",
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)
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```
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const client = new ModelClient(
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"https://<resource>.services.ai.azure.com/models",
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new DefaultAzureCredential(),
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"mistral-large-2407",
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clientOptions,
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);
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```
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Then, you can use the package to consume the model. The following example shows how to create a client to consume chat completions with Entra ID:
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```csharp
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var credential = new DefaultAzureCredential();
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TokenCredential credential = new DefaultAzureCredential();
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AzureAIInferenceClientOptions clientOptions = new AzureAIInferenceClientOptions();
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BearerTokenAuthenticationPolicy tokenPolicy = new BearerTokenAuthenticationPolicy(credential, new string[] { "https://cognitiveservices.azure.com/.default" });
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clientOptions.AddPolicy(tokenPolicy, HttpPipelinePosition.PerRetry);
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ChatCompletionsClient client = new ChatCompletionsClient(
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new Uri("https://<resource>.services.ai.azure.com/models"),
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credential,
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"mistral-large-2407",
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clientOptions.
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);
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```
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ChatCompletionsClient client = new ChatCompletionsClientBuilder()
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.credential(defaultCredential)
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.endpoint("https://<resource>.services.ai.azure.com/models")
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.model("mistral-large-2407")
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.buildClient();
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```
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