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articles/application-gateway/ingress-controller-migration.md

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## Enable AGIC add-on using your existing Application Gateway
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You can now enable the AGIC add-on in your AKS cluster to target your existing Application Gateway through Azure CLI or Portal. Run the following Azure CLI command to enable the AGIC add-on in your AKS cluster. The example enables the add-on in a cluster called *myCluster*, in a resource group called *myResourceGroup*, using the Application Gateway resource ID *appgwId* we saved in the earlier step.
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```azurecli-interactive
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az aks enable-addons -n myCluster -g myResourceGroup -a ingress-appgw --appgw-id $appgwId
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```
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Alternatively, you can navigate to your AKS cluster in Portal using this [link](https://portal.azure.com/?feature.aksagic=true) and enable the AGIC add-on in the Networking tab of your cluster. Select your existing Application Gateway from the dropdown menu when you choose which Application Gateway the add-on should target.
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![Application Gateway Ingress Controller Portal](./media/tutorial-ingress-controller-add-on-existing/portal-ingress-controller-add-on.png)
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## Next Steps
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- [**Application Gateway Ingress Controller Troubleshooting**](ingress-controller-troubleshoot.md): Troubleshooting guide for AGIC
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- [**Application Gateway Ingress Controller Annotations**](ingress-controller-annotations.md): List of annotations on AGIC
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- [**Application Gateway Ingress Controller Annotations**](ingress-controller-annotations.md): List of annotations on AGIC

articles/application-gateway/tutorial-ingress-controller-add-on-existing.md

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az aks enable-addons -n myCluster -g myResourceGroup -a ingress-appgw --appgw-id $appgwId
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```
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## Enable the AGIC add-on in existing AKS cluster through Azure portal
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If you'd like to use Azure portal to enable AGIC add-on, go to [(https://aka.ms/azure/portal/aks/agic)](https://aka.ms/azure/portal/aks/agic) and navigate to your AKS cluster through the portal link. From there, go to the Networking tab within your AKS cluster. You'll see an application gateway ingress controller section, which allows you to enable/disable the ingress controller add-on using the Azure portal. Select the box next to **Enable ingress controller**, and then select the application gateway you created, **myApplicationGateway** from the dropdown menu. Select **Save**.
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> [!IMPORTANT]
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> When you use an application gateway in a different resource group than the AKS cluster resource group, the managed identity **_ingressapplicationgateway-{AKSNAME}_** that is created must have **Contributor** and **Reader** roles set in the application gateway resource group.
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:::image type="content" source="./media/tutorial-ingress-controller-add-on-existing/portal-ingress-controller-add-on.png" alt-text="Screenshot showing how to enable application gateway ingress controller from the networking page of the Azure Kubernetes Service.":::
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## Peer the two virtual networks together
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Since you deployed the AKS cluster in its own virtual network and the Application gateway in another virtual network, you'll need to peer the two virtual networks together in order for traffic to flow from the Application gateway to the pods in the cluster. Peering the two virtual networks requires running the Azure CLI command two separate times, to ensure that the connection is bi-directional. The first command will create a peering connection from the Application gateway virtual network to the AKS virtual network; the second command will create a peering connection in the other direction.

articles/cost-management-billing/savings-plan/download-savings-plan-price-sheet.md

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# Download your savings plan price sheet
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This article explains how you can download the price sheet for an Enterprise Agreement (EA) or Microsoft Customer Agreement (MCA). Your price sheet contains pricing for savings plans.
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This article explains how you can download the price sheet for an Enterprise Agreement (EA) or Microsoft Customer Agreement (MCA) via the Azure portal. Included in the price sheet is the list of products that are eligible for savings plans, as well as the 1- and 3-year savings plans prices for these products.
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## Download EA price sheet
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- [Who can manage a savings plan](manage-savings-plan.md#who-can-manage-a-savings-plan)
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- [How saving plan discount is applied](discount-application.md)
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- [Understand savings plan costs and usage](utilization-cost-reports.md)
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- [Software costs not included with Azure savings plans](software-costs-not-included.md)
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- [Software costs not included with Azure savings plans](software-costs-not-included.md)

articles/machine-learning/azure-machine-learning-glossary.md

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Machine Learning supports two types of environments: curated and custom.
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Curated environments are provided by Machine Learning and are available in your workspace by default. They're intended to be used as is. They contain collections of Python packages and settings to help you get started with various machine learning frameworks. These precreated environments also allow for faster deployment time. For a full list, see [Azure Machine Learning curated environments](resource-curated-environments.md).
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Curated environments are provided by Machine Learning and are available in your workspace by default. They're intended to be used as is. They contain collections of Python packages and settings to help you get started with various machine learning frameworks. These precreated environments also allow for faster deployment time. To retrieve a full list of available environments, see [Azure Machine Learning environments with the CLI & SDK (v2)](/azure/machine-learning/how-to-manage-environments-v2?view=azureml-api-2&tabs=cli&preserve-view=true#curated-environments).
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In custom environments, you're responsible for setting up your environment. Make sure to install the packages and any other dependencies that your training or scoring script needs on the compute. Machine Learning allows you to create your own environment by using:
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## Model
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Machine Learning models consist of the binary files that represent a machine learning model and any corresponding metadata. You can create models from a local or remote file or directory. For remote locations, `https`, `wasbs`, and `azureml` locations are supported. The created model is tracked in the workspace under the specified name and version. Machine Learning supports three types of storage format for models:
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Machine Learning models consist of the binary files that represent a machine learning model and any corresponding metadata. You can create models from a local or remote file or directory. For remote locations, `https`, `wasbs`, and `azureml` locations are supported. The created model is tracked in the workspace under the specified name and version. Machine Learning supports three types of storage formats for models:
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* `custom_model`
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* `mlflow_model`

articles/virtual-machines/extensions/oms-windows.md

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| Agent version | VM extension version | Release date | Release notes |
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| --- | --- | --- | --- |
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| 10.20.18076.0 | 1.0.18076 | September 2024 | - Support for TLS 1.3 and small patches |
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| 10.20.18076.0 | 1.0.18076 | March 2024 | - Support for TLS 1.3 and small patches |
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| 10.20.18069.0 | 1.0.18069 | September 2023 | - Rebuilt the agent to resign then and to replace and expired certificates, Added deprication message to installer |
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| 10.20.18067.0 | 1.0.18067 | March 2022 | - Bug fix for performance counters <br> - Enhancements to Agent Troubleshooter |
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| 10.20.18064.0 | 1.0.18064 | December 2021 | - Bug fix for intermittent crashes |

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