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Merge branch 'main' of github.com:MicrosoftDocs/azure-ai-docs-pr into sdg-freshness
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‎.github/policies/disallow-edits.yml

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- payloadType: Pull_Request
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- isAction:
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action: Opened
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- or:
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- filesMatchPattern:
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matchAny: true
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pattern: articles/index.*
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- filesMatchPattern:
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matchAny: true
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pattern: articles/index.*
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then:
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- addReply:
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reply: >-
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@${issueAuthor} - You tried to add an index file to this repository; this is not permitted so your pull request will be closed automatically.
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@${issueAuthor} - You tried to add an index file to this repository; this is not permitted so your pull request will be closed automatically.
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- closePullRequest
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- description: Close PRs to the "ai-services/personalizer" folder where the author isn't a member of the MicrosoftDocs org (i.e. PRs in public repo).
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if:
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- payloadType: Pull_Request
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- isAction:
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action: Opened
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- filesMatchPattern:
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matchAny: true
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pattern: articles/ai-services/personalizer/*
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- not:
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activitySenderHasAssociation:
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association: Member
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then:
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- addReply:
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reply: >-
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@${issueAuthor} - Pull requests that modify files in this folder aren't accepted from public contributors.
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- closePullRequest
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- description: \@mention specific people when a PR is opened in the "ai-services/personalizer" folder.
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if:
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- payloadType: Pull_Request
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- isAction:
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action: Opened
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- filesMatchPattern:
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matchAny: true
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pattern: articles/ai-services/personalizer/*
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- activitySenderHasAssociation:
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association: Member
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- not:
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or:
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- isActivitySender:
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user: eric-urban
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- isActivitySender:
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user: nitinme
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- isActivitySender:
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user: mrbullwinkle
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then:
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- addReply:
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reply: >-
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@${issueAuthor} - Please don't sign off on this PR. The area owners will sign off once they've reviewed your contribution.
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- mentionUsers:
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mentionees:
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- eric-urban
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- nitinme
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- mrbullwinkle
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replyTemplate: ${mentionees} - Please review this PR and sign off when you're ready to merge it.
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assignMentionees: True # This part probably won't work since the bot doesn't have write perms.

‎articles/ai-foundry/.openpublishing.redirection.ai-studio.json

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"redirect_url": "/azure/ai-foundry/how-to/deploy-models-cohere-command",
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"redirect_document_id": true
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},
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{
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"source_path_from_root": "/articles/ai-foundry/how-to/deploy-models-cohere-command.md",
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"redirect_url": "/azure/ai-foundry/concepts/models-featured#cohere",
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"redirect_document_id": false
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"redirect_url": "/azure/ai-foundry/how-to/deploy-models-cohere-embed",
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"redirect_document_id": true
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"source_path_from_root": "/articles/ai-foundry/how-to/deploy-models-cohere-embed.md",
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"redirect_url": "/azure/ai-foundry/concepts/models-featured#cohere",
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"redirect_document_id": false
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"redirect_url": "/azure/ai-foundry/how-to/deploy-models-cohere-rerank",
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"redirect_document_id": true
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{
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"source_path_from_root": "/articles/ai-foundry/how-to/deploy-models-cohere-rerank.md",
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"redirect_url": "/azure/ai-foundry/concepts/models-featured#cohere-rerank",
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"redirect_document_id": false
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{
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"source_path_from_root": "/articles/ai-studio/how-to/deploy-models-deepseek.md",
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"redirect_url": "/azure/ai-foundry/how-to/deploy-models-deepseek",
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"redirect_document_id": true
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},
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{
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"redirect_url": "/azure/ai-foundry/concepts/models-featured#deepseek",
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"redirect_document_id": false
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{
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"source_path_from_root": "/articles/ai-studio/how-to/deploy-models-gretel-navigator.md",
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"redirect_url": "/azure/ai-foundry/how-to/deploy-models-gretel-navigator",
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"redirect_url": "/azure/ai-foundry/how-to/deploy-models-jais",
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"redirect_document_id": true
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},
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{
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"source_path_from_root": "/articles/ai-foundry/how-to/deploy-models-jais.md",
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"redirect_url": "/azure/ai-foundry/concepts/models-featured#core42",
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"redirect_document_id": false
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{
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"source_path_from_root": "/articles/ai-studio/how-to/deploy-models-jamba.md",
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"redirect_url": "/azure/ai-foundry/how-to/deploy-models-jamba",
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"redirect_document_id": true
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},
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{
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"source_path_from_root": "/articles/ai-foundry/how-to/deploy-models-jamba.md",
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"redirect_url": "/azure/ai-foundry/concepts/models-featured#ai21-labs",
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"redirect_document_id": false
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{
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"source_path_from_root": "/articles/ai-studio/how-to/deploy-models-llama.md",
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"redirect_url": "/azure/ai-foundry/how-to/deploy-models-llama",
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"redirect_document_id": true
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},
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{
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"source_path_from_root": "/articles/ai-foundry/how-to/deploy-models-llama.md",
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"redirect_url": "/azure/ai-foundry/concepts/models-featured#meta",
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"redirect_document_id": false
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{
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"source_path_from_root": "/articles/ai-studio/how-to/deploy-models-managed.md",
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"redirect_url": "/azure/ai-foundry/how-to/deploy-models-managed",
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"redirect_url": "/azure/ai-foundry/how-to/deploy-models-mistral-codestral",
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"redirect_document_id": true
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},
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"redirect_document_id": false
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"source_path_from_root": "/articles/ai-studio/how-to/deploy-models-mistral-nemo.md",
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"redirect_url": "/azure/ai-foundry/how-to/deploy-models-mistral-nemo",
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"redirect_document_id": true
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},
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{
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"source_path_from_root": "/articles/ai-foundry/how-to/deploy-models-mistral-nemo.md",
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"redirect_url": "/azure/ai-foundry/concepts/models-featured#mistral-ai",
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"redirect_document_id": false
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{
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"source_path_from_root": "/articles/ai-studio/how-to/deploy-models-mistral-open.md",
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"redirect_url": "/azure/ai-foundry/how-to/deploy-models-mistral-open",
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"redirect_url": "/azure/ai-foundry/how-to/deploy-models-mistral",
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"redirect_document_id": true
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},
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{
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"source_path_from_root": "/articles/ai-foundry/how-to/deploy-models-mistral.md",
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"redirect_url": "/azure/ai-foundry/concepts/models-featured#mistral-ai",
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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/how-to/deploy-models-openai.md",
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"redirect_url": "/azure/ai-foundry/how-to/deploy-models-openai",
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"redirect_url": "/azure/ai-foundry/how-to/deploy-models-phi-3-5-vision",
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"redirect_document_id": true
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},
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{
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"source_path_from_root": "/articles/ai-foundry/how-to/deploy-models-phi-3-5-vision.md",
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"redirect_url": "/azure/ai-foundry/concepts/models-featured#microsoft",
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"redirect_document_id": false
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{
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"source_path_from_root": "/articles/ai-studio/how-to/deploy-models-phi-3-vision.md",
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"redirect_url": "/azure/ai-foundry/how-to/deploy-models-phi-3-vision",
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"redirect_document_id": true
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{
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"source_path_from_root": "/articles/ai-foundry/how-to/deploy-models-phi-3-vision.md",
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"redirect_url": "/azure/ai-foundry/concepts/models-featured#microsoft",
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"redirect_document_id": false
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{
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"source_path_from_root": "/articles/ai-studio/how-to/deploy-models-phi-3.md",
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"redirect_url": "/azure/ai-foundry/how-to/deploy-models-phi-3",
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"redirect_document_id": true
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},
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{
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"source_path_from_root": "/articles/ai-foundry/how-to/deploy-models-phi-3.md",
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"redirect_url": "/azure/ai-foundry/concepts/models-featured#microsoft",
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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/how-to/deploy-models-phi-4.md",
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"redirect_url": "/azure/ai-foundry/how-to/deploy-models-phi-4",
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"redirect_document_id": true
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},
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{
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"source_path_from_root": "/articles/ai-foundry/how-to/deploy-models-phi-4.md",
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"redirect_url": "/azure/ai-foundry/concepts/models-featured#microsoft",
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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/how-to/deploy-models-serverless-availability.md",
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"redirect_url": "/azure/ai-foundry/how-to/deploy-models-serverless-availability",
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"redirect_url": "/azure/ai-foundry/how-to/deploy-models-timegen-1",
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"redirect_document_id": true
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},
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{
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"source_path_from_root": "/articles/ai-foundry/how-to/deploy-models-timegen-1.md",
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"redirect_url": "/azure/ai-foundry/concepts/models-featured#nixtla",
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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/how-to/deploy-models-tsuzumi.md",
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"redirect_url": "/azure/ai-foundry/how-to/deploy-models-tsuzumi",
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"redirect_document_id": true
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},
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{
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"source_path_from_root": "/articles/ai-foundry/how-to/deploy-models-tsuzumi.md",
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"redirect_url": "/azure/ai-foundry/concepts/models-featured#ntt-data",
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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/how-to/deploy-stability-models.md",
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"redirect_url": "/azure/ai-foundry/how-to/deploy-stability-models",
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"redirect_url": "/azure/ai-foundry/context/context.json",
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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/responsible-use-of-ai-overview.md",
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"redirect_url": "/azure/ai-foundry/responsible-use-of-ai-overview",
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"source_path_from_root": "/articles/ai-studio/how-to/develop/evaluate-sdk.md#cloud-evaluation-on-test-datasets",
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"redirect_url": "/azure/ai-studio/how-to/develop/cloud-evaluation",
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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-foundry/model-inference/reference/reference-model-inference-api.md",
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"redirect_url": "/rest/api/aifoundry/modelinference",
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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-foundry/model-inference/reference/reference-model-inference-info.md",
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"redirect_url": "/rest/api/aifoundry/model-inference/get-model-info/get-model-info",
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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-foundry/model-inference/reference/reference-model-inference-embeddings.md",
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"redirect_url": "/rest/api/aifoundry/model-inference/get-embeddings/get-embeddings",
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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-foundry/model-inference/reference/reference-model-inference-chat-completions.md",
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"redirect_url": "/rest/api/aifoundry/model-inference/get-chat-completions/get-chat-completions",
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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-foundry/model-inference/reference/reference-model-inference-images-embeddings.md",
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"redirect_url": "/rest/api/aifoundry/model-inference/get-image-embeddings/get-image-embeddings",
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"redirect_document_id": false
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}
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]
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}

‎articles/ai-foundry/concepts/concept-model-distillation.md

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manager: scottpolly
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ms.service: azure-ai-foundry
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ms.topic: how-to
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ms.date: 12/15/2024
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ms.date: 03/09/2025
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ms.reviewer: vkann
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ms.author: ssalgado

‎articles/ai-foundry/concepts/concept-synthetic-data.md

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ms.date: 12/15/2024
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ms.date: 03/11/2025
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‎articles/ai-foundry/concepts/encryption-keys-portal.md

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# Customer-managed keys for encryption with Azure AI Foundry
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Customer-managed keys (CMKs) in Azure AI Foundry portal provide enhanced control over the encryption of your data. By using CMKs, you can manage your own encryption keys to add an extra layer of protection and meet compliance requirements more effectively.
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Customer-managed keys (CMKs) in [Azure AI Foundry portal](https://ai.azure.com/) provide enhanced control over the encryption of your data. By using CMKs, you can manage your own encryption keys to add an extra layer of protection and meet compliance requirements more effectively.
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## About encryption in Azure AI Foundry portal
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|-----|-----|-----|
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|Azure Cosmos DB|Stores metadata for your Azure AI projects and tools|Index names, tags; Flow creation timestamps; deployment tags; evaluation metrics|
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|Azure AI Search|Stores indices that are used to help query your Azure AI Foundry content.|An index based off your model deployment names|
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|Azure Storage Account|Stores instructions for how customization tasks are orchestrated|JSON representation of flows you create in Azure AI Foundry portal|
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|Azure Storage Account|Stores instructions for how customization tasks are orchestrated|JSON representation of flows you create in [Azure AI Foundry portal](https://ai.azure.com/)|
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>[!IMPORTANT]
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> Azure AI Foundry uses Azure compute that is managed in the Microsoft subscription, for example when you fine-tune models or or build flows. Its disks are encrypted with Microsoft-managed keys. Compute is ephemeral, meaning after a task is completed the virtual machine is deprovisioned, and the OS disk is deleted. Compute instance machines used for 'Code' experiences are persistant. Azure Disk Encryption isn't supported for the OS disk.

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