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Copy file name to clipboardExpand all lines: articles/active-directory-b2c/partner-deduce.md
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@@ -31,7 +31,7 @@ With this integration, organizations can extend their Azure AD B2C capabilities
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To get started, you'll need:
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- An Azure subscription. If you don't have one, get a [free account](https://azure.microsoft.com/free).
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- An Azure subscription. If you don't have one, get a [free account](https://azure.microsoft.com/pricing/purchase-options/azure-account?cid=msft_learn).
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- An [Azure AD B2C tenant](tutorial-create-tenant.md) that is linked to your Azure subscription.
Copy file name to clipboardExpand all lines: articles/api-management/api-management-howto-llm-logs.md
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## Prerequisites
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- An Azure API Management instance.
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- A managed LLM chat completions API integrated with Azure API Management. For example, [Import an Azure AI Foundry API](azure-ai-foundry-api.md).
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- A managed LLM chat completions API integrated with Azure API Management. For example, [Import a Microsoft Foundry API](azure-ai-foundry-api.md).
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- Access to an Azure Log Analytics workspace.
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- Appropriate permissions to configure diagnostic settings and access logs in API Management.
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:::image type="content" source="media/api-management-howto-llm-logs/llm-log-query-small.png" alt-text="Screenshot of query results for LLM logs in the portal." lightbox="media/api-management-howto-llm-logs/llm-log-query.png":::
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## Upload data to Azure AI Foundry for model evaluation
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## Upload data to Microsoft Foundry for model evaluation
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You can export LLM logging data as a dataset for [model evaluation](/azure/ai-foundry/concepts/observability) in Azure AI Foundry. With model evaluation, you can assess the performance of your generative AI models and applications against a test model or dataset using built-in or custom evaluation metrics.
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You can export LLM logging data as a dataset for [model evaluation](/azure/ai-foundry/concepts/observability) in Microsoft Foundry. With model evaluation, you can assess the performance of your generative AI models and applications against a test model or dataset using built-in or custom evaluation metrics.
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To use LLM logs as a dataset for model evaluation:
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1. Join LLM request and response messages into a single record for each interaction, as shown in the [previous section](#review-azure-monitor-logs-for-requests-and-responses). Include the fields you want to use for model evaluation.
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1. Export the dataset to CSV format, which is compatible with Azure AI Foundry.
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1. In the Azure AI Foundry portal, create a new evaluation to upload and evaluate the dataset.
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1. Export the dataset to CSV format, which is compatible with Microsoft Foundry.
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1. In the Microsoft Foundry portal, create a new evaluation to upload and evaluate the dataset.
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For details to create and run a model evaluation in Azure AI Foundry, see [Evaluate generative AI models and applications by using Azure AI Foundry](/azure/ai-foundry/how-to/evaluate-generative-ai-app).
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For details to create and run a model evaluation in Microsoft Foundry, see [Evaluate generative AI models and applications by using Microsoft Foundry](/azure/ai-foundry/how-to/evaluate-generative-ai-app).
Copy file name to clipboardExpand all lines: articles/api-management/automate-portal-deployments.md
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> Using the script to migrate developer portal content between an API Management instance in a classic tier (for example, Standard) and an instance in a v2 tier (for example, Standard v2) isn't currently supported. Migration of portal content between instances in the v2 tiers is also not supported.
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> [!NOTE]
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> If you're using a self-hosted developer portal with an explicitly defined custom storage account to host media files (that is, you define the `blobStorageUrl` setting in the `config.design.json` configuration file), you need to use the [original `scripts.v2/migrate.js` script](https://github.com/Azure/api-management-developer-portal/blob/master/scripts.v2/migrate.js). The original script doesn't work for managed or self-hosted portals with the media storage account managed by API Management. In that case, use the script from the `/scripts.v3` folder instead.
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> If you're using a self-hosted developer portal with an explicitly defined custom storage account to host media files (that is, you define the `blobStorageUrl` setting in the `config.design.json` configuration file), you need to use the [original `scripts.v3/migrate.js` script](https://github.com/Azure/api-management-developer-portal/blob/master/scripts.v3/migrate.js). The original script doesn't work for managed or self-hosted portals with the media storage account managed by API Management. In that case, use the script from the `/scripts.v3` folder instead.
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