editorial review

Author: msakande

articles/ai-foundry/how-to/deploy-models-managed-pay-go.md

@@ -52,33 +52,31 @@ In this article, you learn how to use protected models from partners and communi
 
 ## Subscribe and deploy on managed compute
 
-1.  Sign in to [Azure AI Foundry](https://ai.azure.com/?cid=learnDocs) and go to the Home page.
+[!INCLUDE [open-catalog](../includes/open-catalog.md)]
 
-1.  Select Model catalog from the left sidebar.
+1. Select the **Deployment options** filter in the model catalog and choose **Managed compute**.
 
-1.  In the filters section, select the Deployment Option as Managed Compute.
+1. Filter the list further by selecting the **Collection** and model of your choice. In this article, we use **Cohere Command A** for illustration.
 
-1.  Select the Collection and model of your choice. In this article, we are using **Cohere Command A** as an example.
+1. From the model's page, select **Use this model** to open the deployment wizard.
 
-1.  Click on **Use this model** and pick the Managed Compute deployment option.
+1. Choose from one of the supported VM SKUs for the model. You need to have Azure Machine Learning Compute quota for that SKU in your Azure subscription.
 
-1.  The Deploy wizard lets you choose from one of the supported VM SKUs for the model. You need to have Azure Machine Learning Compute quota for that SKU in your Azure subscription.
-
-1.  You can then customize your deployment configuration for parameters such as the instance count and select an existing endpoint for the deployment or create a new one. For this example, we consider an instance count of **1** and create a new endpoint for the deployment.
+1. Select **Customize** to specify your deployment configuration for parameters such as the instance count. You can also select an existing endpoint for the deployment or create a new one. For this example, we specify an instance count of **1** and create a new endpoint for the deployment.
 
   :::image type="content" source="media/deploy-models-managed-pay-go/deployment-configuration.png" alt-text="Screenshot of the deployment configuration screen for a protected model in Azure AI Foundry." lightbox="media/deploy-models-managed-pay-go/deployment-configuration.png":::
 
-1.  Click **Next** to proceed to the pricing breakdown page.
+1. Select **Next** to proceed to the *pricing breakdown* page.
 
-1.  Review the pricing breakdown for the deployment, terms of use and license agreement associated with the model's offer on Marketplace. The pricing breakdown helps inform what the aggregated pricing for the model deployed would be, where the surcharge for the model is a function of the number of GPUs in the VM instance that is selected in the previous steps. In addition to the applicable surcharge for the model, Azure Compute charges also apply based on your deployment configuration. If you have existing reservations or azure savings plan, the invoice for the compute charges will honor and reflect the discounted VM pricing.
+1. Review the pricing breakdown for the deployment, terms of use, and license agreement associated with the model's offer on Azure Marketplace. The pricing breakdown tells you what the aggregated pricing for the deployed model would be, where the surcharge for the model is a function of the number of GPUs in the VM instance that is selected in the previous steps. In addition to the applicable surcharge for the model, Azure compute charges also apply, based on your deployment configuration. If you have existing reservations or Azure savings plan, the invoice for the compute charges honors and reflects the discounted VM pricing.
 
   :::image type="content" source="media/deploy-models-managed-pay-go/pricing-breakdown.png" alt-text="Screenshot of the pricing breakdown page for a protected model deployment in Azure AI Foundry." lightbox="media/deploy-models-managed-pay-go/pricing-breakdown.png":::
 
-1.  Select the checkbox to acknowledge understanding of pricing and terms of use, and then, click **Deploy**. It takes about 15-20 mins for the deployment to complete.
+1. Select the checkbox to acknowledge that you understand and agree to the terms of use. Then, select **Deploy**. It takes about 15-20 minutes for the deployment to complete.
 
 ## Network Isolation of deployments
 
-Collections in the model catalog can be deployed within your isolated networks using workspace managed virtual network. For more information on how to configure your workspace managed networks, see [here.](/azure/machine-learning/how-to-managed-network#configure-a-managed-virtual-network-to-allow-internet-outbound)
+Collections in the model catalog can be deployed within your isolated networks using workspace managed virtual network. For more information on how to configure your workspace managed networks, see [Configure a managed virtual network to allow internet outbound](../../machine-learning/how-to-managed-network.md#configure-a-managed-virtual-network-to-allow-internet-outbound).
 
 #### Limitation