Update deploy-models-serverless.md

Author: santiagxf

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

@@ -79,6 +79,17 @@ In this article, you learn how to deploy a model from the model catalog as a ser
     )
     ```
 
+    # [Bicep](#tab/bicep)
+
+    Install the Azure CLI as described at [Azure CLI](/cli/azure/).
+
+    Configure the following environment variables according to your settings:
+
+    ```azurecli
+    RESOURCE_GROUP="serverless-models-dev"
+    LOCATION="eastus2" 
+    ```  
+
     # [ARM](#tab/arm)
 
     You can use any compatible web browser to [deploy ARM templates](../../azure-resource-manager/templates/deploy-portal.md) in the Microsoft Azure portal or use any of the deployment tools. This tutorial uses the [Azure CLI](/cli/azure/).
@@ -108,6 +119,9 @@ The next section covers the steps for subscribing your project to a model offeri
 
 For non-Microsoft models offered through the Azure Marketplace, you can deploy them to serverless API endpoints to consume their predictions. If it's your first time deploying the model in the project, you have to subscribe your project for the particular model offering from the Azure Marketplace. Each project has its own subscription to the particular Azure Marketplace offering of the model, which allows you to control and monitor spending.
 
+> [!TIP]
+> Skip this step if you are deploying models from the Phi-3 family of models. Directly [deploy the model to a serverless API endpoint](#deploy-the-model-to-a-serverless-api-endpoint).
+
 > [!NOTE]
 > Models offered through the Azure Marketplace are available for deployment to serverless API endpoints in specific regions. Check [Model and region availability for Serverless API deployments](deploy-models-serverless-availability.md) to verify which models and regions are available. If the one you need is not listed, you can deploy to a workspace in a supported region and then [consume serverless API endpoints from a different workspace](deploy-models-serverless-connect.md).
 
@@ -158,11 +172,41 @@ For non-Microsoft models offered through the Azure Marketplace, you can deploy t
     ).result()
     ```
 
+    # [Bicep](#tab/bicep)
+
+    Use the following bicep configuration to create a model subscription:
+
+    __model-subscription.bicep__
+    
+    ```bicep
+    param projectName string = 'my-project'
+    param modelId string = 'azureml://registries/azureml-meta/models/Meta-Llama-3-8B-Instruct'
+    
+    var modelName = substring(modelId, (lastIndexOf(modelId, '/') + 1))
+    var subscriptionName = '${modelName}-subscription'
+    
+    resource projectName_subscription 'Microsoft.MachineLearningServices/workspaces/marketplaceSubscriptions@2024-04-01-preview' = if (!startsWith(
+      modelId,
+      'azureml://registries/azureml/'
+    )) {
+      name: '${projectName}/${subscriptionName}'
+      properties: {
+        modelId: modelId
+      }
+    }
+    ```
+
+    Then create the resource as follows:
+
+    ```azurecli
+    az deployment group create --resource-group $RESOURCE_GROUP --template-file model-subscription.bicep
+    ```
+
     # [ARM](#tab/arm)
 
     Use the following template to create a model subscription:
 
-    __template.json__
+    __model-subscription.json__
 
     ```json
     {
@@ -196,6 +240,12 @@ For non-Microsoft models offered through the Azure Marketplace, you can deploy t
     }
     ```
 
+    Use the Azure portal or the Azure CLI to create the deployment.
+
+    ```azurecli
+    az deployment group create --resource-group $RESOURCE_GROUP --template-file model-subscription.json
+    ```
+
 1. Once you subscribe the project for the particular Azure Marketplace offering, subsequent deployments of the same offering in the same project don't require subscribing again.
 
 1. At any point, you can see the model offers to which your project is currently subscribed:
@@ -227,6 +277,15 @@ For non-Microsoft models offered through the Azure Marketplace, you can deploy t
         print(sub.as_dict())
     ```
 
+    # [Bicep](#tab/bicep)
+
+    You can use the resource management tools to query the resources. The following code uses Azure CLI:
+
+    ```azurecli
+    az resource list \
+        --query "[?type=='Microsoft.SaaS']"
+    ```
+
     # [ARM](#tab/arm)
 
     You can use the resource management tools to query the resources. The following code uses Azure CLI:
@@ -292,6 +351,46 @@ In this section, you create an endpoint with the name **meta-llama3-8b-qwerty**.
     ).result()
     ```
 
+    # [Bicep](#tab/bicep)
+
+    Use the following template to create an endpoint:
+
+    __serverless-endpoint.bicep__
+
+    ```bicep
+    param projectName string = 'my-project'
+    param endpointName string = 'myserverless-text-1234ss'
+    param location string = resourceGroup().location
+    param modelId string = 'azureml://registries/azureml-meta/models/Meta-Llama-3-8B-Instruct'
+    
+    var modelName = substring(modelId, (lastIndexOf(modelId, '/') + 1))
+    var subscriptionName = '${modelName}-subscription'
+    
+    resource projectName_endpoint 'Microsoft.MachineLearningServices/workspaces/serverlessEndpoints@2024-04-01-preview' = {
+      name: '${projectName}/${endpointName}'
+      location: location
+      sku: {
+        name: 'Consumption'
+      }
+      properties: {
+        modelSettings: {
+          modelId: modelId
+        }
+      }
+      dependsOn: [
+        projectName_subscription
+      ]
+    }
+    
+    output endpointUri string = projectName_endpoint.properties.inferenceEndpoint.uri
+    ```
+
+    Create the deployment as follows:
+
+    ```azurecli
+    az deployment group create --resource-group $RESOURCE_GROUP --template-file model-subscription.bicep
+    ```
+
     # [ARM](#tab/arm)
 
     Use the following template to create an endpoint:
@@ -344,8 +443,7 @@ In this section, you create an endpoint with the name **meta-llama3-8b-qwerty**.
 
     ```azurecli
     az deployment group create \
-        --name model-subscription-deployment \
-        --resource-group <resource-group> \
+        --resource-group $RESOURCE_GROUP \
         --template-file template.json
     ```
 
@@ -386,6 +484,15 @@ In this section, you create an endpoint with the name **meta-llama3-8b-qwerty**.
     ).result()
     ```
 
+    # [Bicep](#tab/bicep)
+
+    You can use the resource management tools to query the resources. The following code uses Azure CLI:
+
+    ```azurecli
+    az resource list \
+        --query "[?type=='Microsoft.MachineLearningServices/workspaces/serverlessEndpoints']"
+    ```
+
     # [ARM](#tab/arm)
 
     You can use the resource management tools to query the resources. The following code uses Azure CLI:
@@ -418,6 +525,10 @@ In this section, you create an endpoint with the name **meta-llama3-8b-qwerty**.
     print(endpoint_keys.secondary_key)
     ```
 
+    # [Bicep](#tab/bicep)
+
+    Use REST APIs to query this information.
+
     # [ARM](#tab/arm)
 
     Use REST APIs to query this information.
@@ -435,6 +546,18 @@ Models deployed in Azure Machine Learning and Azure AI studio in Serverless API
 
 Read more about the [capabilities of this API](../reference/reference-model-inference-api.md#capabilities) and how [you can use it when building applications](../reference/reference-model-inference-api.md#getting-started). 
 
+## Network isolation
+
+Endpoints for models deployed as Serverless APIs follow the public network access (PNA) flag setting of the AI Studio Hub that has the project in which the deployment exists. To secure your MaaS endpoint, disable the PNA flag on your AI Studio Hub. You can secure inbound communication from a client to your endpoint by using a private endpoint for the hub.
+
+To set the PNA flag for the Azure AI hub:
+
+1. Go to the [Azure portal](https://portal.azure.com).
+2. Search for the Resource group to which the hub belongs, and select your Azure AI hub from the resources listed for this Resource group.
+3. On the hub Overview page, use the left navigation pane to go to Settings > Networking.
+4. Under the **Public access** tab, you can configure settings for the public network access flag.
+5. Save your changes. Your changes might take up to five minutes to propagate.
+
 ## Delete endpoints and subscriptions
 
 You can delete model subscriptions and endpoints. Deleting a model subscription makes any associated endpoint become *Unhealthy* and unusable.
@@ -494,6 +617,15 @@ To delete the associated model subscription:
 client.marketplace_subscriptions.begin_delete(subscription_name).wait()
 ```
 
+# [Bicep](#tab/bicep)
+
+You can use the resource management tools to manage the resources. The following code uses Azure CLI:
+
+```azurecli
+az resource delete --name <resource-name>
+```
+
+
 # [ARM](#tab/arm)
 
 You can use the resource management tools to manage the resources. The following code uses Azure CLI: