Merge pull request #2902 from santiagxf/santiagxf-patch-1

Update bicep.md

Author: prmerger-automator[bot]

articles/ai-foundry/model-inference/how-to/quickstart-create-resources.md

@@ -26,7 +26,3 @@ zone_pivot_groups: azure-ai-models-deployment
 ::: zone pivot="programming-language-bicep"
 [!INCLUDE [bicep](../includes/create-resources/bicep.md)]
 ::: zone-end
-
-## Next steps
-
-* [Develop applications using Azure AI model inference service in Azure AI services](../supported-languages.md)

articles/ai-foundry/model-inference/includes/create-resources/bicep.md

@@ -40,6 +40,8 @@ The tutorial helps you create:
 > * (Optionally) An Azure AI project and hub.
 > * (Optionally) A connection between the hub and the models in Azure AI Services.
 
+Notice that **you have to deploy an Azure AI project and hub** if you plan to use the Azure AI Foundry portal for managing the resource, using playground, or any other feature from the portal.
+
 You are using the following assets to create those resources:
 
 1. Use the template `modules/ai-services-template.bicep` to describe your Azure AI Services resource:
@@ -54,7 +56,7 @@ You are using the following assets to create those resources:
 
     :::code language="bicep" source="~/azureai-model-inference-bicep/infra/modules/ai-services-deployment-template.bicep":::
 
-3. For convenience, we define the model we want to have available in the service using a JSON file. The file [__infra/models.json__](https://github.com/Azure-Samples/azureai-model-inference-bicep/blob/main/infra/models.json) contains a list of JSON object with keys `name`,`version`, `provider`, and `sku`, which defines the models the deployment will provision. Since the models support pay-as-you-go, adding model deployments doesn't incur on extra cost. Modify the file removing/adding the model entries you want to have available. The following example **shows only the first 7 lines** of the JSON file:
+3. For convenience, we define the model we want to have available in the service using a JSON file. The file [__infra/models.json__](https://github.com/Azure-Samples/azureai-model-inference-bicep/blob/main/infra/models.json) contains a list of JSON object with keys `name`,`version`, `provider`, and `sku`, which defines the models the deployment will provision. Since the models support pay-as-you-go, adding model deployments doesn't incur on extra cost. Modify the file by **removing/adding the model entries you want to have available**. The following example **shows only the first 7 lines** of the JSON file:
 
     __models.json__
 
@@ -118,4 +120,9 @@ In your console, follow these steps:
       --template-file deploy.bicep
     ```
 
-7. The template outputs the Azure AI model inference endpoint that you can use to consume any of the model deployments you have created.
+7. The template outputs the Azure AI model inference endpoint that you can use to consume any of the model deployments you have created.
+
+## Next steps
+
+> [!div class="nextstepaction"]
+> [Use the inference endpoint](../../how-to/inference.md)

articles/ai-foundry/model-inference/includes/create-resources/portal.md

@@ -54,5 +54,7 @@ To create a project with an Azure AI Services account, follow these steps:
 
 10. To use Azure AI model inference, you need to add model deployments to your Azure AI services account.
 
-    > [!div class="nextstepaction"]
-    > [Add and configure models](../../how-to/create-model-deployments.md)
+## Next steps
+
+> [!div class="nextstepaction"]
+> [Add and configure models](../../how-to/create-model-deployments.md)