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| `network-secured-agent.bicep` | Deploy a network secured agent setup that uses user-managed identity authentication on the Agent connections. | [](https://portal.azure.com/#create/Microsoft.Template/uri/https%3A%2F%2Fraw.githubusercontent.com%2FAzure%2Fazure-quickstart-templates%2Frefs%2Fheads%2Fmaster%2Fquickstarts%2Fmicrosoft.azure-ai-agent-service%2Fnetwork-secured-agent%2Fazuredeploy.json)
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| `network-secured-agent.bicep` | Deploy a network secured agent setup that uses user-managed identity authentication on the Agent connections. | [](https://portal.azure.com/#create/Microsoft.Template/uri/https%3A%2F%2Fraw.githubusercontent.com%2FAzure-Samples%2Fazureai-samples%2Fmain%2Fscenarios%2FAgents%2Fsetup%2Fnetwork-secured-agent%2Fazuredeploy.json)
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### Option 2: manually deploy the bicep template
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Make sure you have the Azure AI Developer role for the resource group you created.
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1. Using the resource group you created in the previous step and one of the template files (either `basic-agent-keys.bicep` or `basic-agent-identity.bicep`), run one of the following commands:
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1. Using the resource group you created in the previous step and one of the template files (`network-secured-agent`), run one of the following commands:
Copy file name to clipboardExpand all lines: articles/ai-services/openai/concepts/content-filter.md
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ms.author: pafarley
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ms.service: azure-ai-openai
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ms.topic: conceptual
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ms.date: 02/20/2025
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ms.date: 02/27/2025
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ms.custom: template-concept, devx-track-python
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manager: nitinme
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In addition to detection on last user content, Azure OpenAI also supports the detection of specific risks inside context documents via Prompt Shields – Indirect Prompt Attack Detection. You should identify parts of the input that are a document (for example, retrieved website, email, etc.) with the following document delimiter.
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```
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<documents>
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*insert your document content here*
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</documents>
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\"\"\" <documents> *insert your document content here* </documents> \"\"\"
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```
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When you do so, the following options are available for detection on tagged documents:
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- Apply for modified content filters via [this form](https://ncv.microsoft.com/uEfCgnITdR).
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- Azure OpenAI content filtering is powered by [Azure AI Content Safety](https://azure.microsoft.com/products/cognitive-services/ai-content-safety).
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- Learn more about understanding and mitigating risks associated with your application: [Overview of Responsible AI practices for Azure OpenAI models](/legal/cognitive-services/openai/overview?context=/azure/ai-services/openai/context/context).
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- Learn more about how data is processed in connection with content filtering and abuse monitoring: [Data, privacy, and security for Azure OpenAI Service](/legal/cognitive-services/openai/data-privacy?context=/azure/ai-services/openai/context/context#preventing-abuse-and-harmful-content-generation).
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- Learn more about how data is processed in connection with content filtering and abuse monitoring: [Data, privacy, and security for Azure OpenAI Service](/legal/cognitive-services/openai/data-privacy?context=/azure/ai-services/openai/context/context#preventing-abuse-and-harmful-content-generation).
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| Models | Description |
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|--|--|
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|[GPT-4.5 Preview](#gpt-45-preview)|The latest GPT model that excels at diverse text and image tasks. |
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|[o-series models](#o-series-models)|[Reasoning models](../how-to/reasoning.md) with advanced problem-solving and increased focus and capability. |
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|[GPT-4o & GPT-4o mini & GPT-4 Turbo](#gpt-4o-and-gpt-4-turbo)| The latest most capable Azure OpenAI models with multimodal versions, which can accept both text and images as input. |
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|[GPT-4o audio](#gpt-4o-audio)| GPT-4o audio models that support either low-latency, "speech in, speech out" conversational interactions or audio generation. |
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|[Whisper](#whisper-models)| A series of models in preview that can transcribe and translate speech to text. |
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|[Text to speech](#text-to-speech-models-preview) (Preview) | A series of models in preview that can synthesize text to speech. |
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## GPT-4.5 Preview
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### Availability
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**For access to `gpt-4.5-preview` registration is required, and access will be granted based on Microsoft's eligibility criteria**. Customers who have access to other limited access models will still need to request access for this model.
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Request access: [GPT-4.5-preview limited access model application](https://aka.ms/oai/gptaccess)
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Once access has been granted, you will need to create a deployment for the model
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### Region Availability
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| Model | Region |
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|---|---|
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|`gpt-4.5-preview`| East US 2 (Global Standard) <br> Sweden Central (Global Standard) |
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### Capabilities
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| Model ID | Description | Context Window | Max Output Tokens | Training Data (up to) |
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| --- | :--- |:--- |:---|:---: |
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|`gpt-4.5-preview` (2025-02-27) <br> **GPT-4.5 Preview**| The **latest GPT model** that excels at diverse text and image tasks. <br>-Structured outputs <br>-Prompt caching <br>-Tools <br>-Streaming<br>-Text(input/output)<br>- Image(input) | 128,000 | 16,384 | Oct 2023 |
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> [!NOTE]
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> It is expected behavior that the model cannot answer questions about itself. If you want to know when the knowledge cutoff for the model's training data is, or other details about the model you should refer to the model documentation above.
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## o-series models
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The Azure OpenAI o<sup>*</sup> series models are specifically designed to tackle reasoning and problem-solving tasks with increased focus and capability. These models spend more time processing and understanding the user's request, making them exceptionally strong in areas like science, coding, and math compared to previous iterations.
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- GPT-4o real-time audio is designed to handle real-time, low-latency conversational interactions, making it a great fit for support agents, assistants, translators, and other use cases that need highly responsive back-and-forth with a user. For more information on how to use GPT-4o real-time audio, see the [GPT-4o real-time audio quickstart](../realtime-audio-quickstart.md) and [how to use GPT-4o audio](../how-to/realtime-audio.md).
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- GPT-4o audio completion is designed to generate audio from audio or text prompts, making it a great fit for generating audio books, audio content, and other use cases that require audio generation. The GPT-4o audio completions model introduces the audio modality into the existing `/chat/completions` API. For more information on how to use GPT-4o audio completions, see the [audio generation quickstart](../audio-completions-quickstart.md).
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> [!CAUTION]
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> We don't recommend using preview models in production. We will upgrade all deployments of preview models to either future preview versions or to the latest stable GA version. Models that are designated preview don't follow the standard Azure OpenAI model lifecycle.
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To use GPT-4o audio, you need [an Azure OpenAI resource](../how-to/create-resource.md) in one of the [supported regions](#global-standard-model-availability).
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When your resource is created, you can [deploy](../how-to/create-resource.md#deploy-a-model) the GPT-4o audio model.
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- references_regions
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- ignite-2024
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ms.topic: whats-new
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ms.date: 2/19/2025
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ms.date: 2/27/2025
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recommendations: false
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---
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## February 2025
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### GPT-4.5 Preview
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The latest GPT model that excels at diverse text and image tasks is now available on Azure OpenAI.
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**For access to `gpt-4.5-preview` registration is required, and access will be granted based on Microsoft's eligibility criteria**. Customers who have access to other limited access models will still need to request access for this model.
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Request access: [GPT-4.5-preview limited access model application](https://aka.ms/oai/gptaccess)
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For more information on model capabilities, and region availability see the [models documentation](./concepts/models.md#gpt-45-preview).
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### Stored completions API
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[Stored completions](./how-to/stored-completions.md#stored-completions-api) allow you to capture the conversation history from chat completions sessions to use as datasets for evaluations and fine-tuning.
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### o3-mini datazone standard deployments
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`o3-mini` is now available for global standard, and data zone standard deployments for registered limited access customers. Data standard deployment regions are currently United States regions only.
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`o3-mini` is now available for global standard, and data zone standard deployments for registered limited access customers.
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For more information, see our [reasoning model guide](./how-to/reasoning.md).
Copy file name to clipboardExpand all lines: articles/machine-learning/how-to-authenticate-batch-endpoint.md
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1. Go to [Azure Machine Learning studio](https://ml.azure.com).
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1. Navigate to __Compute__, then __Compute clusters__, and select the compute cluster your deployment is using.
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1. Navigate to __Compute__, then __Compute clusters__.
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1. Select the compute cluster your deployment is using. This action opens the compute cluster's __Details__ page.
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1. Assign a managed identity to the compute cluster:
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1.In the __Managed identity__ section, verify if the compute has a managed identity assigned. If not, select the option __Edit__.
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1.Go to the __Managed identity__ section of the page and verify if the compute has a managed identity assigned. If not, select the pencil icon to edit the managed identity.
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1. Select __Assign a managed identity__ and configure it as needed. You can use a System-Assigned Managed Identity or a User-Assigned Managed Identity. If using a System-Assigned Managed Identity, it is named as "[workspace name]/computes/[compute cluster name]".
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1. Select the slider next to __Assign a managed identity__ to enable and configure it as needed. You can use a System-Assigned Managed Identity or a User-Assigned Managed Identity. If using a System-Assigned Managed Identity, it is named as "[workspace name]/computes/[compute cluster name]".
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