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.openpublishing.redirection.json

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"redirect_url": "/azure/azure-maps/",
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"redirect_document_id": true
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},
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{
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"source_path_from_root": "/articles/azure-maps/tutorial-creator-indoor-maps.md",
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"redirect_url": "/azure/azure-maps/creator-indoor-maps",
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"redirect_document_id": false
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},
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{
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"source_path_from_root": "/articles/azure-maps/about-creator.md",
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"redirect_url": "/azure/azure-maps/creator-indoor-maps",
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"redirect_document_id": true
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},
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{
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"source_path_from_root": "/articles/azure-maps/search-categories.md",
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"redirect_url": "/azure/azure-maps/supported-search-categories",

.openpublishing.redirection.operator-nexus.json

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"source_path_from_root": "/articles/operator-nexus/template-cloud-native-network-function-deployment.md",
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"redirect_url": "/azure/operator-nexus/quickstarts-kubernetes-cluster-deployment-cli",
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"redirect_document_id": false
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},
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{
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"source_path_from_root": "/articles/operator-nexus/concepts-nexus-network.md",
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"redirect_url": "/azure/operator-nexus/concepts-nexus-networking",
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"redirect_document_id": false
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}
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]
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}
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}

articles/ai-studio/how-to/model-catalog-overview.md

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Model Catalog offers two distinct ways to deploy models from the catalog for your use: managed compute and serverless APIs. The deployment options available for each model vary; learn more about the features of the deployment options, and the options available for specific models, in the following tables. Learn more about [data processing]( concept-data-privacy.md) with the deployment options.
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<!-- docutune:disable -->
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Features | Managed compute | serverless API (pay-as-you-go)
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Features | Managed compute | Serverless API (pay-as-you-go)
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--|--|--
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Deployment experience and billing | Model weights are deployed to dedicated Virtual Machines with Managed Online Endpoints. The managed online endpoint, which can have one or more deployments, makes available a REST API for inference. You're billed for the Virtual Machine core hours used by the deployments. | Access to models is through a deployment that provisions an API to access the model. The API provides access to the model hosted and managed by Microsoft, for inference. This mode of access is referred to as "Models as a Service". You're billed for inputs and outputs to the APIs, typically in tokens; pricing information is provided before you deploy.
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Deployment experience and billing | Model weights are deployed to dedicated Virtual Machines with Managed Online Endpoints. The managed online endpoint, which can have one or more deployments, makes available a REST API for inference. You're billed for the Virtual Machine core hours used by the deployments. | Access to models is through a deployment that provisions an API to access the model. The API provides access to the model hosted and managed by Microsoft, for inference. You're billed for inputs and outputs to the APIs, typically in tokens; pricing information is provided before you deploy.
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| API authentication | Keys and Microsoft Entra ID authentication.| Keys only.
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Content safety | Use Azure Content Safety service APIs. | Azure AI Content Safety filters are available integrated with inference APIs. Azure AI Content Safety filters may be billed separately.
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Network isolation | [Configure managed networks for Azure AI Studio hubs.](configure-managed-network.md) | MaaS endpoint will follow your hub's public network access (PNA) flag setting. For more information, see the [Network isolation for models deployed via Serverless APIs](#network-isolation-for-models-deployed-via-serverless-apis) section.
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Content safety | Use Azure Content Safety service APIs. | Azure AI Content Safety filters are available integrated with inference APIs. Azure AI Content Safety filters is billed separately.
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Network isolation | [Configure managed networks for Azure AI Studio hubs.](configure-managed-network.md) | Endpoints will follow your hub's public network access (PNA) flag setting. For more information, see the [Network isolation for models deployed via Serverless APIs](#network-isolation-for-models-deployed-via-serverless-apis) section.
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Model | Managed compute | Serverless API (pay-as-you-go)
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## Serverless APIs with Pay-as-you-go billing
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Certain models in the Model Catalog can be deployed as serverless APIs with pay-as-you-go billing; this method of deployment is called Models-as-a Service (MaaS), providing a way to consume them as an API without hosting them on your subscription. Models available through MaaS are hosted in infrastructure managed by Microsoft, which enables API-based access to the model provider's model. API based access can dramatically reduce the cost of accessing a model and significantly simplify the provisioning experience. Most MaaS models come with token-based pricing.
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Certain models in the Model Catalog can be deployed as serverless APIs with pay-as-you-go billing, providing a way to consume them as an API without hosting them on your subscription. Models are hosted in infrastructure managed by Microsoft, which enables API-based access to the model provider's model. API based access can dramatically reduce the cost of accessing a model and significantly simplify the provisioning experience.
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### How are third-party models made available in MaaS?
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Models that are available for deployment as serverless APIs with pay-as-you-go billing are offered by the model provider but hosted in Microsoft-managed Azure infrastructure and accessed via API. Model providers define the license terms and set the price for use of their models, while Azure Machine Learning service manages the hosting infrastructure, makes the inference APIs available, and acts as the data processor for prompts submitted and content output by models deployed via MaaS. Learn more about data processing for MaaS at the [data privacy](concept-data-privacy.md) article.
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:::image type="content" source="../media/explore/model-publisher-cycle.png" alt-text="A diagram showing model publisher service cycle." lightbox="../media/explore/model-publisher-cycle.png":::
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Models that are available for deployment as serverless APIs with pay-as-you-go billing are offered by the model provider but hosted in Microsoft-managed Azure infrastructure and accessed via API. Model providers define the license terms and set the price for use of their models, while Azure Machine Learning service manages the hosting infrastructure, makes the inference APIs available, and acts as the data processor for prompts submitted and content output by models deployed via MaaS. Learn more about data processing for MaaS at the [data privacy](concept-data-privacy.md) article.
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### Pay for model usage in MaaS
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### Billing
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The discovery, subscription, and consumption experience for models deployed via MaaS is in the Azure AI Studio and Azure Machine Learning studio. Users accept license terms for use of the models, and pricing information for consumption is provided during deployment. Models from third party providers are billed through Azure Marketplace, in accordance with the [Commercial Marketplace Terms of Use](/legal/marketplace/marketplace-terms); models from Microsoft are billed using Azure meters as First Party Consumption Services. As described in the [Product Terms](https://www.microsoft.com/licensing/terms/welcome/welcomepage), First Party Consumption Services are purchased using Azure meters but aren't subject to Azure service terms; use of these models is subject to the license terms provided.
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### Deploy models for inference through MaaS
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Deploying a model through MaaS allows users to get access to ready to use inference APIs without the need to configure infrastructure or provision GPUs, saving engineering time and resources. These APIs can be integrated with several LLM tools and usage is billed as described in the previous section.
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### Fine-tune models through MaaS with Pay-as-you-go
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### Fine-tune models
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For models that are available through MaaS and support fine-tuning, users can take advantage of hosted fine-tuning with pay-as-you-go billing to tailor the models using data they provide. For more information, see the [fine-tuning overview](../concepts/fine-tuning-overview.md).
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Certain models support also serverless fine-tuning where users can take advantage of hosted fine-tuning with pay-as-you-go billing to tailor the models using data they provide. For more information, see the [fine-tuning overview](../concepts/fine-tuning-overview.md).
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### RAG with models deployed as serverless APIs
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articles/aks/index.yml

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# Cards and links should be based on top customer tasks or top subjects
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# Start card title with a verb
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# Card (optional)
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- title: About Azure Kubernetes Service (AKS)
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- title: About AKS
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linkLists:
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- linkListType: overview
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links:
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- text: What is AKS?
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url: what-is-aks.md
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- text: What is AKS Automatic?
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url: intro-aks-automatic.md
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- text: Get started with AKS
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url: /azure/architecture/reference-architectures/containers/aks-start-here
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- linkListType: whats-new
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- text: Supported Kubernetes versions
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url: supported-kubernetes-versions.md
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- linkListType: concept
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links:
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- text: Core concepts
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url: core-aks-concepts.md
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- linkListType: reference
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links:
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- text: Trusted Launch (preview)
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url: use-trusted-launch.md
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- text: Azure Container Storage (preview)
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url: /azure/storage/container-storage/container-storage-introduction
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- text: Deploy an AI model on AKS with the AI toolchain operator (Preview)
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url: ai-toolchain-operator.md
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- text: Upgrade multiple AKS clusters using Azure Kubernetes Fleet Manager
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url: /azure/kubernetes-fleet/update-orchestration
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- text: Reduce image pull time with Artifact Streaming on AKS (Preview)
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url: artifact-streaming.md
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- text: Secure your cluster using pod security policies (Deprecated)
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url: use-pod-security-policies.md
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- text: Automatically upgrade node images
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url: auto-upgrade-node-image.md
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- text: Azure Linux Container Host for AKS
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url: use-azure-linux.md
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- text: Vertical Pod Autoscaler
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url: vertical-pod-autoscaler.md
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- text: Use Confidential Virtual Machines
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url: use-cvm.md
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- text: Azure CLI
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url: /cli/azure/aks
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- text: Azure PowerShell
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url: /powershell/module/az.aks
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- text: REST
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url: /rest/api/aks/
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# Card (optional)
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- title: Evaluate Linux-based AKS cluster
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- title: Get started
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linkLists:
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- linkListType: quickstart
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links:
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- text: AKS Automatic
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url: ./learn/quick-kubernetes-automatic-deploy.md
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- text: Azure CLI
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- text: Create a Linux cluster
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url: ./learn/quick-kubernetes-deploy-cli.md
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- text: Azure PowerShell
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url: ./learn/quick-kubernetes-deploy-powershell.md
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- text: Azure portal
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url: ./learn/quick-kubernetes-deploy-portal.md
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- text: Resource Manager template
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url: ./learn/quick-kubernetes-deploy-rm-template.md
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- text: Terraform
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url: ./learn/quick-kubernetes-deploy-terraform.md
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# Card (optional)
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- title: Evaluate application deployment on AKS
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linkLists:
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- text: Create a Windows cluster
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url: ./learn/quick-windows-container-deploy-cli.md
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- linkListType: tutorial
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- text: 1. Prepare an application for AKS
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- text: Prepare an application for AKS
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url: tutorial-kubernetes-prepare-app.md
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- text: 2. Deploy and use Azure Container Registry
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url: tutorial-kubernetes-prepare-acr.md
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- text: 3. Deploy an AKS cluster
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url: tutorial-kubernetes-deploy-cluster.md
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- text: 4. Run your application
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url: tutorial-kubernetes-deploy-application.md
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- text: 5. Scale applications
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url: tutorial-kubernetes-scale.md
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- text: 6. Update an application
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url: tutorial-kubernetes-app-update.md
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- text: 7. Upgrade Kubernetes in AKS
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url: tutorial-kubernetes-upgrade-cluster.md
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# Card
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- title: Evaluate Windows-based AKS cluster
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linkLists:
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- linkListType: concept
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links:
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- text: Windows Server containers FAQ
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url: windows-faq.md
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- linkListType: quickstart
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links:
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- text: Create a Windows Server container using the Azure CLI
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url: learn/quick-windows-container-deploy-cli.md
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- text: Create a Windows Server container using the Azure PowerShell
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url: learn/quick-windows-container-deploy-powershell.md
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# Card
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- title: Evaluate application deployment methods
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linkLists:
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- linkListType: tutorial
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- linkListType: training
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links:
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- text: Develop with Helm
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url: quickstart-helm.md
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- text: Develop with Dapr
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url: quickstart-dapr.md
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- text: Use Draft and the DevX extension for Visual Studio Code
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url: draft-devx-extension-aks.md
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- text: Use Automated Deployments
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url: automated-deployments.md
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# Card
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- title: Architecture guidance
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- text: AKS training modules
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url: /training/browse/?products=azure-kubernetes-service
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# Card (optional)
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- title: Plan and design
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- linkListType: architecture
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- text: Baseline architecture
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url: /azure/architecture/reference-architectures/containers/aks/secure-baseline-aks?WT.mc_id=AKSDOCSPAGE
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- text: Baseline for microservices
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url: /azure/architecture/reference-architectures/containers/aks-microservices/aks-microservices-advanced?WT.mc_id=AKSDOCSPAGE
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- text: Baseline for PCI-DSS 3.2.1
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url: /azure/architecture/reference-architectures/containers/aks-pci/aks-pci-intro?WT.mc_id=AKSDOCSPAGE
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- text: Baseline for multiregion
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url: /azure/architecture/reference-architectures/containers/aks-multi-region/aks-multi-cluster?WT.mc_id=AKSDOCSPAGE
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# Card
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- title: Production deployment guidance
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linkLists:
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- linkListType: deploy
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links:
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- text: AKS Landing Zone accelerator
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url: /azure/cloud-adoption-framework/scenarios/app-platform/aks/landing-zone-accelerator
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- text: AKS baseline cluster reference implementation (GitHub)
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url: https://github.com/mspnp/aks-baseline
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# Card
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- title: Extend the capabilities of your cluster
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linkLists:
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- text: Architectural best practices
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url: /azure/well-architected/service-guides/azure-kubernetes-service
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- text: Baseline infrastructure architecture
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url: /azure/architecture/reference-architectures/containers/aks/baseline-aks
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- linkListType: concept
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links:
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- text: Istio add-on
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url: istio-about.md
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- text: Dapr cluster extension
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url: dapr.md
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- text: Cluster extensions
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url: cluster-extensions.md
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- text: GitHub Actions for AKS
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url: integrations.md#github-actions
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# Card
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- title: Operations guidance
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linkLists:
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- linkListType: how-to-guide
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links:
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- text: Day-2 operations guide
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url: /azure/architecture/operator-guides/aks/day-2-operations-guide?WT.mc_id=AKSDOCSPAGE
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- text: Best practices for cluster operators and developers
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url: best-practices.md
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- text: Plan how to monitor AKS
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url: monitor-aks.md
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- text: Plan how to monitor AKS cluster networks
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url: network-observability-overview.md
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# Card
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- title: Troubleshooting
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linkLists:
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- linkListType: how-to-guide
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links:
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- text: Troubleshooting Guides
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url: /troubleshoot/azure/azure-kubernetes/welcome-azure-kubernetes
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- text: Troubleshoot create operations
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url: /troubleshoot/azure/azure-kubernetes//troubleshoot-aks-cluster-creation-issues
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- text: Troubleshoot common issues
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url: /troubleshoot/azure/azure-kubernetes/aks-common-issues-faq
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- text: Optimize costs
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url: best-practices-cost.md
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- text: Windows container considerations
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url: windows-vs-linux-containers.md

articles/api-center/TOC.yml

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- name: 3 - Add environments and deployments
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- name: 4 - Discover shadow APIs
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- name: Analyze APIs using linting rules
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- name: Discover shadow APIs with Dev Proxy
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href: discover-shadow-apis-dev-proxy.md
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- name: API discovery and consumption
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- name: Self-host Azure API Center portal

articles/api-center/discover-shadow-apis-dev-proxy.md

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---
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title: Tutorial - Discover shadow APIs using Dev Proxy
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description: In this tutorial, you learn how to discover shadow APIs in your apps using Dev Proxy and onboard them to API Center.
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title: Discover shadow APIs using Dev Proxy
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description: Learn how to discover shadow APIs in your apps using Dev Proxy and onboard them to API Center.
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# Discover shadow APIs using Dev Proxy
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Using Azure API Center you catalog APIs used in your organization. This allows you to tell which APIs you use, where the API is in its lifecycle, and who to contact if there are issues. In short, having an up-to-date catalog of APIs helps you improve the governance-, compliance-, and security posture.
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articles/api-center/overview.md

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* **Real-world API representation** - Add real-world information about each API including versions and definitions such as OpenAPI definitions. List API deployments and associate them with runtime environments, for example, representing Azure API Management or other API management solutions.
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* **API governance** - Organize and filter APIs and related resources using built-in and custom metadata, to help with API governance and discovery by API consumers. Set up linting and analysis to enforce API definition quality.
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* **API governance** - Organize and filter APIs and related resources using built-in and custom metadata, to help with API governance and discovery by API consumers. Set up [linting and analysis](enable-api-analysis-linting.md) to enforce API definition quality. Integrate with tools such as Dev Proxy to ensure that apps don't use unregistered [shadow APIs](discover-shadow-apis-dev-proxy.md) or APIs that don't meet organizational standards.
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* **API discovery and reuse** - Enable developers and API program managers to discover APIs via the Azure portal, an API Center portal, and developer tools including a [Visual Studio Code extension](use-vscode-extension.md)​.
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