Merge branch 'main' into apic-devproxy-articles

Author: waldekmastykarz

.openpublishing.publish.config.json

@@ -1225,6 +1225,12 @@
       "url": "https://github.com/MicrosoftDocs/SupportArticles-docs",
       "branch": "main",
       "branch_mapping": {}
+    },
+    {
+      "path_to_root": "msdocs-python-fastapi-webapp-quickstart",
+      "url": "https://github.com/Azure-Samples/msdocs-python-fastapi-webapp-quickstart",
+      "branch": "main",
+      "branch_mapping": {}
     }
   ],
   "branch_target_mapping": {

.openpublishing.redirection.baremetal-infrastructure.json

@@ -134,6 +134,11 @@
             "source_path_from_root": "/articles/baremetal-infrastructure/workloads/sap/baremetal-overview-architecture.md",
             "redirect_url": "/azure/baremetal-infrastructure/concepts-baremetal-infrastructure-overview",
             "redirect_document_id": false
-        }
+        },
+        {
+            "source_path": "articles/baremetal-infrastructure/workloads/nc2-on-azure/solution-design.md",
+            "redirect_url": "/azure/baremetal-infrastructure/workloads/nc2-on-azure/architecture",
+            "redirect_document_id": false
+        }       
     ]
 }

.openpublishing.redirection.json

@@ -1189,6 +1189,16 @@
             "redirect_url": "/azure/azure-maps/",
             "redirect_document_id": true
         },
+        {
+            "source_path_from_root": "/articles/azure-maps/tutorial-creator-indoor-maps.md",
+            "redirect_url": "/azure/azure-maps/creator-indoor-maps",
+            "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/azure-maps/about-creator.md",
+            "redirect_url": "/azure/azure-maps/creator-indoor-maps",
+            "redirect_document_id": true
+        },
         {
             "source_path_from_root": "/articles/azure-maps/search-categories.md",
             "redirect_url": "/azure/azure-maps/supported-search-categories",

.openpublishing.redirection.operator-nexus.json

@@ -29,6 +29,11 @@
             "source_path_from_root": "/articles/operator-nexus/template-cloud-native-network-function-deployment.md",
             "redirect_url": "/azure/operator-nexus/quickstarts-kubernetes-cluster-deployment-cli",
             "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/operator-nexus/concepts-nexus-network.md",
+            "redirect_url": "/azure/operator-nexus/concepts-nexus-networking",
+            "redirect_document_id": false
         }
     ]
-}
+}

articles/ai-services/openai/how-to/deployment-types.md

@@ -33,7 +33,7 @@ Azure OpenAI offers three types of deployments. These provide a varied level of
 | **Best suited for**      | Applications that don’t require data residency. Recommended starting place for customers. | For customers with data residency requirements. Optimized for low to medium volume. | Real-time scoring for large consistent volume. Includes the highest commitments and limits.|
 | **How it works**         | Traffic may be routed anywhere in the world | | |
 | **Getting started**      | [Model deployment](./create-resource.md) | [Model deployment](./create-resource.md) | [Provisioned onboarding](./provisioned-throughput-onboarding.md) |
-| **Cost**                 | [Baseline](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/) | [Regional Pricing](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/) | May experience cost savings for consistent usage |
+| **Cost**                 | [Global deployment pricing](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/) | [Regional pricing](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/) | May experience cost savings for consistent usage |
 | **What you get**         | Easy access to all new models with highest default pay-per-call limits.<br><br> Customers with high volume usage may see higher latency variability | Easy access with [SLA on availability](https://azure.microsoft.com/support/legal/sla/). Optimized for low to medium volume workloads with high burstiness. <br><br>Customers with high consistent volume may experience greater latency variability. | Regional access with very high & predictable throughput. Determine throughput per PTU using the provided [capacity calculator](./provisioned-throughput-onboarding.md#estimate-provisioned-throughput-and-cost) |
 | **What you don’t get**   | ❌Data residency guarantees | ❌High volume w/consistent low latency | ❌Pay-per-call flexibility |
 | **Per-call Latency**     | Optimized for real-time calling & low to medium volume usage. Customers with high volume usage may see higher latency variability. Threshold set per model | Optimized for real-time calling & low to medium volume usage. Customers with high volume usage may see higher latency variability. Threshold set per model | Optimized for real-time. |

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

@@ -47,12 +47,12 @@ Some models in the **Curated by Azure AI** and **Open models from the Hugging Fa
 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. 
 <!-- docutune:disable -->
 
-Features | Managed compute | serverless API (pay-as-you-go) 
+Features | Managed compute | Serverless API (pay-as-you-go) 
 --|--|-- 
-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.  
+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.  
 | API authentication   | Keys and Microsoft Entra ID authentication.| Keys only.  
-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.  
-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.
+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.  
+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.
 
 Model | Managed compute | Serverless API (pay-as-you-go) 
 --|--|--
@@ -100,25 +100,19 @@ Prompt flow offers a great experience for prototyping. You can use models deploy
 
 ## Serverless APIs with Pay-as-you-go billing
 
-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.   
+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.
 
-### How are third-party models made available in MaaS?
+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. 
 
 :::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":::
 
-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. 
-
-### Pay for model usage in MaaS    
+### Billing   
 
 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.  
 
-### Deploy models for inference through MaaS 
-
-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. 
-
-### Fine-tune models through MaaS with Pay-as-you-go 
+### Fine-tune models
 
-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).
+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).
 
 ### RAG with models deployed as serverless APIs 
 

articles/aks/TOC.yml

@@ -182,7 +182,7 @@
       href: concepts-sustainable-software-engineering.md
     - name: Preview API life cycle
       href: concepts-preview-api-life-cycle.md
-    - name: Dapr
+    - name: Dapr extension
       href: dapr-overview.md
     - name: GitOps
       href: ../azure-arc/kubernetes/conceptual-gitops-flux2.md?toc=/azure/aks/toc.json&bc=/azure/aks/breadcrumb/toc.json
@@ -879,7 +879,7 @@
               href: deploy-application-az-cli.md
         - name: Dapr
           items:
-            - name: Create the Dapr extension
+            - name: Install the Dapr extension
               href: dapr.md
             - name: Configure the Dapr extension
               href: dapr-settings.md

articles/aks/dapr-overview.md

@@ -1,12 +1,12 @@
 ---
-title: Dapr extension for Azure Kubernetes Service (AKS) overview
+title: Dapr extension for Azure Kubernetes Service (AKS) and Arc-enabled Kubernetes
 description: Learn more about using Dapr on your Azure Kubernetes Service (AKS) cluster to develop applications.
 ms.author: nickoman
 ms.topic: overview
 ms.date: 04/22/2024
 ---
 
-# Dapr
+# Dapr extension for Azure Kubernetes Service (AKS) and Arc-enabled Kubernetes
 
 [Distributed Application Runtime (Dapr)][dapr-docs] offers APIs that help you write and implement simple, portable, resilient, and secured microservices. Dapr APIs run as a sidecar process in tandem with your applications and abstract away common complexities you may encounter when building distributed applications, such as:
 - Service discovery
@@ -19,7 +19,7 @@ Dapr is incrementally adoptable. You can use any of the API building blocks as n
 
 ## Capabilities and features
 
-[Using the Dapr extension to provision Dapr on your AKS or Arc-enabled Kubernetes cluster](../azure-arc/kubernetes/conceptual-extensions.md) eliminates the overhead of:
+[Using the Dapr extension to provision Dapr on your AKS or Arc-enabled Kubernetes cluster][dapr-create-extension] eliminates the overhead of:
 - Downloading Dapr tooling
 - Manually installing and managing the Dapr runtime on your AKS cluster
 
@@ -63,7 +63,7 @@ Microsoft provides best-effort support for [the latest version of Dapr and two p
 - 1.12.x 
 - 1.11.x 
 
-You can run Azure CLI commands to retreive a list of available versions in [a cluster](/cli/azure/k8s-extension/extension-types#az-k8s-extension-extension-types-list-versions-by-cluster) or [a location](/cli/azure/k8s-extension/extension-types#az-k8s-extension-extension-types-list-versions-by-location).
+You can run Azure CLI commands to retrieve a list of available versions in [a cluster](/cli/azure/k8s-extension/extension-types#az-k8s-extension-extension-types-list-versions-by-cluster) or [a location](/cli/azure/k8s-extension/extension-types#az-k8s-extension-extension-types-list-versions-by-location).
 
 To view a list of the stable Dapr versions available to your managed AKS cluster, run the following command:
 
@@ -221,7 +221,9 @@ If you install Dapr through the AKS extension, our recommendation is to continue
 
 ## Next Steps
 
-After learning about Dapr and some of the challenges it solves, try [Deploying an application with the Dapr cluster extension][dapr-quickstart].
+> [!div class="nextstepaction"]
+> [Walk through the Dapr extension quickstart to demo how it works][dapr-quickstart]
+
 
 <!-- Links Internal -->
 [csi-secrets-store]: ./csi-secrets-store-driver.md
@@ -231,6 +233,7 @@ After learning about Dapr and some of the challenges it solves, try [Deploying a
 [dapr-migration]: ./dapr-migration.md
 [aks-msi]: ./use-managed-identity.md
 [dapr-configuration-options]: ./dapr-settings.md
+[dapr-create-extension]: ./dapr.md
 
 <!-- Links External -->
 [dapr-docs]: https://docs.dapr.io/
@@ -248,4 +251,4 @@ After learning about Dapr and some of the challenges it solves, try [Deploying a
 [dapr-subscriptions]: https://docs.dapr.io/developing-applications/building-blocks/pubsub/subscription-methods/#declarative-subscriptions
 [dapr-supported-version]: https://docs.dapr.io/operations/support/support-release-policy/
 [dapr-observability]: https://docs.dapr.io/operations/observability/
-[dapr-alpha-beta]: https://docs.dapr.io/operations/support/alpha-beta-apis/
+[dapr-alpha-beta]: https://docs.dapr.io/operations/support/alpha-beta-apis/

articles/aks/dapr-settings.md

@@ -374,7 +374,9 @@ The Dapr extension requires the following outbound URLs on `https://:443` to fun
 
 ## Next Steps
 
-Once you successfully provisioned Dapr in your AKS cluster, try deploying a [sample application][sample-application].
+- [Walk through the tutorial for deploying Dapr Workflow via the extension][dapr-workflow]
+- [Determine if you need to migrate from Dapr open source to the Dapr extension][dapr-migration].
+
 
 <!-- LINKS INTERNAL -->
 [deploy-cluster]: ./tutorial-kubernetes-deploy-cluster.md
@@ -389,7 +391,7 @@ Once you successfully provisioned Dapr in your AKS cluster, try deploying a [sam
 [dapr-migration]: ./dapr-migration.md
 [dapr-settings]: ./dapr-settings.md
 [aks-azurelinux]: ./cluster-configuration.md#azure-linux-container-host-for-aks
-
+[dapr-workflow]: ./dapr-workflow.md
 
 <!-- LINKS EXTERNAL -->
 [kubernetes-production]: https://docs.dapr.io/operations/hosting/kubernetes/kubernetes-production

articles/aks/dapr-workflow.md

@@ -185,7 +185,8 @@ Notice that the workflow status is marked as completed.
 
 ## Next steps
 
-[Learn how to add configuration settings to the Dapr extension on your AKS cluster][dapr-config].
+- [Configure the Dapr extension on your AKS cluster][dapr-config].
+- [Determine if you need to migrate from Dapr open source to the Dapr extension][dapr-migration].
 
 <!-- Links Internal -->
 [deploy-cluster]: ./tutorial-kubernetes-deploy-cluster.md
@@ -194,6 +195,7 @@ Notice that the workflow status is marked as completed.
 [cluster]: ./tutorial-kubernetes-deploy-cluster.md
 [k8s-sp]: ./dapr.md#register-the-kubernetesconfiguration-resource-provider
 [dapr-config]: ./dapr-settings.md
+[dapr-migration]: ./dapr-migration.md
 [az-cloud-shell]: ./learn/quick-kubernetes-deploy-powershell.md#azure-cloud-shell
 [kubectl]: ./tutorial-kubernetes-deploy-cluster.md#connect-to-cluster-using-kubectl