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Merge pull request #262727 from msakande/updating-author-for-ai-studio-docs
updating author & reviewer fields for Open models in AI studio
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‎articles/ai-studio/concepts/deployments-overview.md‎

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title: Deploy models, flows, and web apps with Azure AI Studio
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titleSuffix: Azure AI Studio
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description: Learn about deploying models, flows, and web apps with Azure AI Studio.
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manager: nitinme
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manager: scottpolly
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ms.service: azure-ai-studio
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ms.custom:
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- ignite-2023
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ms.topic: conceptual
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ms.date: 12/7/2023
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ms.reviewer: eur
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ms.author: eur
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author: eric-urban
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ms.reviewer: fasantia
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ms.author: mopeakande
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author: msakande
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---
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# Overview: Deploy models, flows, and web apps with Azure AI Studio
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## Planning AI safety for a deployed model
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For Azure OpenAI models such as GPT-4, Azure AI Studio provides AI safety filter during the deployment to ensure responsible use of AI. AI content safety filter allows moderation of harmful and sensitive contents to promote the safety of AI-enhanced applications. In addition to AI safety filter, Azure AI Studio offers model monitoring for deployed models. Model monitoring for LLMs uses the latest GPT language models to monitor and alert when the outputs of the model perform poorly against the set thresholds of generation safety and quality. For example, you can configure a monitor to evaluate how well the model’s generated answers align with information from the input source ("groundedness") and closely match to a ground truth sentence or document ("similarity").
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For Azure OpenAI models such as GPT-4, Azure AI Studio provides AI safety filter during the deployment to ensure responsible use of AI. AI content safety filter allows moderation of harmful and sensitive contents to promote the safety of AI-enhanced applications. In addition to AI safety filter, Azure AI Studio offers model monitoring for deployed models. Model monitoring for LLMs uses the latest GPT language models to monitor and alert when the outputs of the model perform poorly against the set thresholds of generation safety and quality. For example, you can configure a monitor to evaluate how well the model's generated answers align with information from the input source ("groundedness") and closely match to a ground truth sentence or document ("similarity").
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## Optimizing the performance of a deployed model
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‎articles/ai-studio/how-to/deploy-models-llama.md‎

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title: How to deploy Llama 2 family of large language models with Azure AI Studio
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titleSuffix: Azure AI Studio
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description: Learn how to deploy Llama 2 family of large language models with Azure AI Studio.
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manager: nitinme
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manager: scottpolly
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ms.service: azure-ai-studio
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ms.topic: how-to
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ms.date: 12/11/2023
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ms.reviewer: eur
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ms.author: eur
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author: eric-urban
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ms.reviewer: fasantia
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ms.author: mopeakande
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author: msakande
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ms.custom: [references_regions]
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‎articles/ai-studio/how-to/deploy-models-open.md‎

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title: How to deploy open models with Azure AI Studio
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titleSuffix: Azure AI Studio
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description: Learn how to deploy open models with Azure AI Studio.
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manager: nitinme
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manager: scottpolly
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ms.service: azure-ai-studio
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ms.topic: how-to
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ms.date: 12/11/2023
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ms.reviewer: eur
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ms.author: eur
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author: eric-urban
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ms.reviewer: fasantia
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ms.author: mopeakande
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author: msakande
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# How to deploy large language models with Azure AI Studio

‎articles/ai-studio/how-to/deploy-models-openai.md‎

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title: How to deploy Azure OpenAI models with Azure AI Studio
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titleSuffix: Azure AI Studio
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description: Learn how to deploy Azure OpenAI models with Azure AI Studio.
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manager: nitinme
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manager: scottpolly
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ms.service: azure-ai-studio
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ms.custom:
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- ignite-2023
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ms.topic: how-to
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ms.date: 12/11/2023
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ms.reviewer: eur
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ms.author: eur
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author: eric-urban
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ms.reviewer: fasantia
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ms.author: mopeakande
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author: msakande
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# How to deploy Azure OpenAI models with Azure AI Studio

‎articles/ai-studio/how-to/monitor-quality-safety.md‎

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title: Monitor quality and safety of deployed applications
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titleSuffix: Azure AI Studio
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description: Learn how to monitor quality and safety of deployed applications with Azure AI Studio.
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manager: nitinme
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manager: scottpolly
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ms.service: azure-ai-studio
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ms.custom:
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- ignite-2023
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ms.topic: how-to
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ms.date: 11/15/2023
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ms.reviewer: eur
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ms.author: eur
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author: eric-urban
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ms.reviewer: fasantia
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ms.author: mopeakande
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author: msakande
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# Monitor quality and safety of deployed applications
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> [!NOTE]
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> Monitoring requires the endpoint to be used at least 10 times to collect enough data to provide insights. If you’d like to test sooner, manually send about 50 rows in the ‘test’ tab before running the monitor.
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> Monitoring requires the endpoint to be used at least 10 times to collect enough data to provide insights. If you'd like to test sooner, manually send about 50 rows in the 'test' tab before running the monitor.
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Create your monitor by either enabling from the deployment details page, or the **Monitoring** tab.
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‎articles/ai-studio/how-to/troubleshoot-deploy-and-monitor.md‎

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title: How to troubleshoot your deployments and monitors in Azure AI Studio
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titleSuffix: Azure AI Studio
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description: This article provides instructions on how to troubleshoot your deployments and monitors in Azure AI Studio.
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manager: nitinme
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manager: scottpolly
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ms.service: azure-ai-studio
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ms.custom:
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- ignite-2023
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ms.topic: how-to
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ms.date: 11/15/2023
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ms.reviewer: eur
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ms.author: eur
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author: eric-urban
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ms.reviewer: fasantia
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ms.author: mopeakande
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author: msakande
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# How to troubleshoot your deployments and monitors in Azure AI Studio
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**Question:** I got the following error message about the deployment failure. What should I do to troubleshoot?
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```
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ResourceNotFound: Deployment failed due to timeout while waiting for Environment Image to become available. Check Environment Build Log in ML Studio Workspace or Workspace storage for potential failures. Image build summary: [N/A]. Environment info: Name: CliV2AnonymousEnvironment, Version: ‘Ver’, you might be able to find the build log under the storage account 'NAME' in the container 'CONTAINER_NAME' at the Path 'PATH/PATH/image_build_aggregate_log.txt'.
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ResourceNotFound: Deployment failed due to timeout while waiting for Environment Image to become available. Check Environment Build Log in ML Studio Workspace or Workspace storage for potential failures. Image build summary: [N/A]. Environment info: Name: CliV2AnonymousEnvironment, Version: 'Ver', you might be able to find the build log under the storage account 'NAME' in the container 'CONTAINER_NAME' at the Path 'PATH/PATH/image_build_aggregate_log.txt'.
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You might have come across an ImageBuildFailure error: This happens when the environment (docker image) is being built. For more information about the error, you can check the build log for your `<CONTAINER NAME>` environment.

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