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.openpublishing.redirection.json

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

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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, 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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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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articles/ai-services/.openpublishing.redirection.ai-services.json

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articles/ai-services/agents/includes/quickstart-csharp.md

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var connectionString = Environment.GetEnvironmentVariable("AZURE_AI_CONNECTION_STRING");
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var connectionString = Environment.GetEnvironmentVariable("PROJECT_CONNECTION_STRING");
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AgentsClient client = new AgentsClient(connectionString, new DefaultAzureCredential());
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articles/ai-services/agents/overview.md

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# What is Azure AI Agent Service?
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# What is Azure AI Agent Service (Preview)?
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[Azure AI Agent Service](https://techcommunity.microsoft.com/blog/azure-ai-services-blog/introducing-azure-ai-agent-service/4298357) is a fully managed service designed to empower developers to securely build, deploy, and scale high-quality, and extensible AI agents without needing to manage the underlying compute and storage resources. What originally took hundreds of lines of code to support [client side function calling](/azure/ai-services/openai/how-to/function-calling) can now be done in just a few lines of code with Azure AI Agent Service.
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articles/ai-services/agents/quickstart.md

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# Quickstart: Create a new agent
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# Quickstart: Create a new agent (Preview)
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Azure AI Agent Service allows you to create AI agents tailored to your needs through custom instructions and augmented by advanced tools like code interpreter, and custom functions.
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articles/ai-services/agents/toc.yml

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articles/ai-services/computer-vision/computer-vision-resource-container-config.md

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* Azure portal: **Azure AI services** Overview; labeled `Endpoint`
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## Example docker commands
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The following examples use the configuration settings to illustrate how to write and use `docker run` commands. Once running, the container continues to run until you [stop](computer-vision-how-to-install-containers.md#stop-the-container) it.
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The following examples use the configuration settings to illustrate how to write and use `docker run` commands. Once running, the container continues to run until you [stop](computer-vision-how-to-install-containers.md#stop-the-container) it.
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* **Line-continuation character**: The Docker commands in the following sections use the back slash, `\`, as a line continuation character. Replace or remove this based on your host operating system's requirements.
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> The `Eula`, `Billing`, and `ApiKey` options must be specified to run the container; otherwise, the container won't start. For more information, see [Billing](computer-vision-how-to-install-containers.md#billing).
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> Review [How to install and run containers](computer-vision-how-to-install-containers.md).

articles/ai-services/computer-vision/concept-background-removal.md

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The background removal feature is available through the [Segment](/rest/api/computervision/image-analysis/segment?view=rest-computervision-2023-02-01-preview&tabs=HTTP) API (`imageanalysis:segment`). See the [Background removal how-to guide](./how-to/background-removal.md) for more information.
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## Next step
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articles/ai-services/computer-vision/concept-categorizing-images.md

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In addition to tags and a description, Image Analysis 3.2 can return the taxonomy-based categories detected in an image. Unlike tags, categories are organized in a parent/child hierarchy, and there are fewer of them (86, as opposed to thousands of tags). All category names are in English. Categorization can be done by itself or alongside the newer tags model.
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In addition to tags and a description, Image Analysis 3.2 can return the taxonomy-based categories detected in an image. Unlike tags, categories are organized in a parent/child hierarchy, and there are fewer of them (86, as opposed to thousands of tags). All category names are in English. Categorization can be done by itself or in the same API call as the newer tags model.
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## Related content
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Learn the related concepts of [tagging images](concept-tagging-images.md) and [describing images](concept-describing-images.md).

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