Merge pull request #3 from sdgilley/sdg-ai-studio-gh

Fix blocking issues

Author: sdgilley

articles/ai-studio/ai-services/concepts/deployment-types.md

@@ -9,7 +9,7 @@ ms.topic: conceptual
 ms.date: 10/24/2024
 ms.author: fasantia
 ms.reviewer: fasantia
-ms.custom: ignite-2024, github-universe-2024
+ms.custom: github-universe-2024
 ---
 
 # Deployment types in Azure AI model inference

articles/ai-studio/ai-services/concepts/endpoints.md

@@ -9,7 +9,7 @@ manager: scottpolly
 ms.date: 10/24/2024
 ms.author: sgilley
 ms.reviewer: fasantia
-ms.custom: ignite-2024, github-universe-2024
+ms.custom: github-universe-2024
 ---
 
 # Use the Azure AI model inference endpoint
@@ -43,7 +43,7 @@ The Azure AI inference endpoint allows customers to use a single endpoint with t
 
 You can see the endpoint URL and credentials in the **Overview** section. The endpoint usually has the form `https://<resource-name>.services.ai.azure.com/models`:
 
-:::image type="content" source="../../media/ai-services/overview/overview-endpoint-and-key.png" alt-text="An screenshot showing how to get the URL and key associated with the resource." lightbox="../../media/ai-services/overview/overview-endpoint-and-key.png":::
+:::image type="content" source="../../media/ai-services/overview/overview-endpoint-and-key.png" alt-text="A screenshot showing how to get the URL and key associated with the resource." lightbox="../../media/ai-services/overview/overview-endpoint-and-key.png":::
 
 ### Routing
 

articles/ai-studio/ai-services/concepts/quotas-limits.md

@@ -3,7 +3,7 @@ title: Azure AI model inference quotas and limits
 titleSuffix: Azure AI services
 description: Quick reference, detailed description, and best practices on the quotas and limits for the Azure AI models service in Azure AI services.
 ms.service: azure-ai-studio
-ms.custom: ignite-2024, github-universe-2024
+ms.custom: github-universe-2024
 ms.topic: conceptual
 author: sdgilley
 manager: scottpolly

articles/ai-studio/ai-services/faq.yml

@@ -91,7 +91,7 @@ sections:
       - question: |
           Does Azure AI model inference service support custom API headers? We append other custom headers to our API requests and are seeing HTTP 431 failure errors.
         answer: |
-          Our current APIs allow up to 10 custom headers, which are passed through the pipeline, and returned. We notice some customers now exceed this header count resulting in HTTP 431 errors. There's no solution for this error, other than to reduce header volume. In future API versions we'll no longer pass through custom headers. We recommend customers not depend on custom headers in future system architectures. 
+          Our current APIs allow up to 10 custom headers, which are passed through the pipeline, and returned. We notice some customers now exceed this header count resulting in HTTP 431 errors. There's no solution for this error, other than to reduce header volume. We recommend customers not depend on custom headers in future system architectures. 
   - name: Pricing and Billing
     questions:
       - question: |

articles/ai-studio/ai-services/how-to/create-model-deployments.md

@@ -38,7 +38,7 @@ To use it:
 
 1. Get the Azure AI model's inference endpoint URL and keys from the **deployment page** or the **Overview** page. If you're using Microsoft Entra ID authentication, you don't need a key.
 
-    :::image type="content" source="../../media/ai-services/add-model-deployments/models-deploy-endpoint-url.png" alt-text="An screenshot showing how to get the URL and key associated with the deployment." lightbox="../../media/ai-services/add-model-deployments/models-deploy-endpoint-url.png":::
+    :::image type="content" source="../../media/ai-services/add-model-deployments/models-deploy-endpoint-url.png" alt-text="A screenshot showing how to get the URL and key associated with the deployment." lightbox="../../media/ai-services/add-model-deployments/models-deploy-endpoint-url.png":::
 
 2. Use the model inference endpoint URL and the keys from before when constructing your client. The following example uses the Azure AI Inference package:
 

articles/ai-studio/ai-services/how-to/quickstart-github-models.md

@@ -5,7 +5,7 @@ description: Learn how to upgrade your endpoint from GitHub Models to Azure AI M
 ms.service: azure-ai-studio
 ms.topic: how-to
 ms.date: 10/01/2024
-ms.custom: ignite-2024, github-universe-2024
+ms.custom: github-universe-2024
 manager: nitinme
 author: mrbullwinkle
 ms.author: fasantia 
@@ -50,7 +50,7 @@ To obtain the key and endpoint:
 
 8. Once it's deployed, your model's API Key and endpoint are shown in the Overview. Use these values in your code to use the model in your production environment.
 
-    :::image type="content" source="../../media/ai-services/add-model-deployments/models-deploy-endpoint-url.png" alt-text="An screenshot showing how to get the URL and key associated with the deployment." lightbox="../../media/ai-services/add-model-deployments/models-deploy-endpoint-url.png":::
+    :::image type="content" source="../../media/ai-services/add-model-deployments/models-deploy-endpoint-url.png" alt-text="A screenshot showing how to get the URL and key associated with the deployment." lightbox="../../media/ai-services/add-model-deployments/models-deploy-endpoint-url.png":::
 
 At this point, the model you selected is ready to consume. 
 
@@ -61,9 +61,9 @@ At this point, the model you selected is ready to consume.
 
 Once your Azure AI Services resource is configured, you can start consuming it from your code. You need the endpoint URL and key for it, which can be found in the **Overview** section:
 
-:::image type="content" source="../../media/ai-services/overview/overview-endpoint-and-key.png" alt-text="An screenshot showing how to get the URL and key associated with the resource." lightbox="../../media/ai-services/overview/overview-endpoint-and-key.png":::
+:::image type="content" source="../../media/ai-services/overview/overview-endpoint-and-key.png" alt-text="A screenshot showing how to get the URL and key associated with the resource." lightbox="../../media/ai-services/overview/overview-endpoint-and-key.png":::
 
-You can use any of the supported SDK's to get predictions out from the endpoint. The following SDK's are officially supported:
+You can use any of the supported SDKs to get predictions out from the endpoint. The following SDKs are officially supported:
 
 * OpenAI SDK
 * Azure OpenAI SDK

articles/ai-studio/ai-services/model-inference.md

@@ -8,7 +8,7 @@ ms.author: fasantia
 ms.service: azure-ai-studio
 ms.topic: overview
 ms.date: 08/14/2024
-ms.custom: ignite-2024, github-universe-2024
+ms.custom: github-universe-2024
 recommendations: false
 ---
 

articles/ai-studio/includes/ai-services/add-model-deployments.md

@@ -24,7 +24,7 @@ You can add all the models you need in the endpoint by using [Azure AI Studio fo
 
 5. For models providers that require extra terms of contract, you're asked to accept those terms. For instance, Mistral models ask you to accept other terms. Accept the terms on those cases by selecting **Subscribe and deploy**.
 
-   :::image type="content" source="../../media/ai-services/add-model-deployments/models-deploy-agree.png" alt-text="An screenshot showing how to agree the terms and conditions of a Mistral-Large model." lightbox="../../media/ai-services/add-model-deployments/models-deploy-agree.png":::
+   :::image type="content" source="../../media/ai-services/add-model-deployments/models-deploy-agree.png" alt-text="A screenshot showing how to agree the terms and conditions of a Mistral-Large model." lightbox="../../media/ai-services/add-model-deployments/models-deploy-agree.png":::
 
 6.  You can configure the deployment settings at this time. By default, the deployment receives the name of the model you're deploying. The deployment name is used in the `model` parameter for request to route to this particular model deployment. This setting allows you to also configure specific names for your models when you attach specific configurations. For instance, `o1-preview-safe` for a model with a strict content safety content filter.