Merge pull request #199823 from MicrosoftDocs/main

Merge main to live, 4 AM

Author: ttorble

.openpublishing.redirection.json

@@ -2718,6 +2718,11 @@
       "redirect_url": "/azure/aks/load-balancer-standard",
       "redirect_document_id": false
     },
+    {
+      "source_path_from_root": "/articles/aks/keda.md",
+      "redirect_url": "/azure/aks/keda-about",
+      "redirect_document_id": false
+    },
     {
       "source_path_from_root": "/articles/analysis-services/analysis-services-create-model-portal.md",
       "redirect_url": "/azure/analysis-services/analysis-services-overview",
@@ -43274,6 +43279,11 @@
       "redirect_url": "/azure/cognitive-services/translator/custom-translator/key-terms",
       "redirect_document_id": false
     },
+    {
+      "source_path_from_root": "/articles/applied-ai-services/form-recognizer/generate-sas-tokens.md",
+      "redirect_url": "/azure/applied-ai-services/form-recognizer/create-sas-tokens",
+      "redirect_document_id": false
+    },
     {
       "source_path_from_root": "/articles/cognitive-services/language-service/text-summarization/how-to/call-api.md",
       "redirect_url": "/azure/cognitive-services/language-service/summarization/how-to/document-summarization",

articles/active-directory-b2c/partner-gallery.md

@@ -21,7 +21,7 @@ Our ISV partner network extends our solution capabilities to help you build seam
 To be considered into this sample documentation, submit your application request in the [Microsoft Application Network portal](https://microsoft.sharepoint.com/teams/apponboarding/Apps/SitePages/Default.aspx). For any additional questions, send an email to [[email protected]](mailto:[email protected]).
 
 >[!NOTE]
->The [Azure Active Directory B2C community site on GitHub](https://azure-ad-b2c.github.io/azureadb2ccommunity.io/) also provides sample custom policies from the community.
+>The [Azure Active Directory B2C community site on GitHub](https://github.com/azure-ad-b2c/partner-integrations) also provides sample custom policies from the community.
 
 ## Identity verification and proofing
 

articles/aks/TOC.yml

@@ -467,10 +467,16 @@
           href: open-service-mesh-uninstall-add-on.md
     - name: Track releases and region availability
       href: release-tracker.md
+    - name: Deploy the Kubernetes Event-driven Autoscaler (KEDA) add-on (preview)
+      items:
+        - name: About Kubernetes Event-driven Autoscaler (KEDA)
+          href: keda-about.md
+        - name: Use ARM template
+          href: keda-deploy-add-on-arm.md
+        - name: Kubernetes Event-driven Autoscaler (KEDA) integrations
+          href: keda-integrations.md
     - name: Use Web Application Routing (preview)
       href: web-app-routing.md
-    - name: Kubernetes Event-driven Autoscaler add-on (preview)
-      href: keda.md
     - name: Use cluster extensions
       href: cluster-extensions.md
     - name: DevOps

articles/aks/includes/keda/current-version-callout.md

@@ -0,0 +1,11 @@
+---
+author: tomkerkhove
+
+ms.service: container-service
+ms.topic: include
+ms.date: 05/24/2022
+ms.author: tomkerkhove
+---
+
+> [!IMPORTANT]
+> The KEDA add-on installs version *2.7.0* of KEDA on your cluster.

articles/aks/keda-about.md

@@ -0,0 +1,79 @@
+---
+title: Kubernetes Event-driven Autoscaling (KEDA) (Preview)
+description: Simplified application autoscaling with Kubernetes Event-driven Autoscaling (KEDA) add-on.
+services: container-service
+author: tomkerkhove
+ms.topic: article
+ms.date: 05/24/2022
+ms.author: tomkerkhove
+---
+
+# Simplified application autoscaling with Kubernetes Event-driven Autoscaling (KEDA) add-on (Preview)
+
+Kubernetes Event-driven Autoscaling (KEDA) is a single-purpose and lightweight component that strives to make application autoscaling simple and is a CNCF Incubation project.
+
+It applies event-driven autoscaling to scale your application to meet demand in a sustainable and cost-efficient manner with scale-to-zero.
+
+The KEDA add-on makes it even easier by deploying a managed KEDA installation, providing you with [a rich catalog of 50+ KEDA scalers][keda-scalers] that you can scale your applications with on your Azure Kubernetes Services (AKS) cluster.
+
+[!INCLUDE [preview features callout](./includes/preview/preview-callout.md)]
+
+## Architecture
+
+[KEDA][keda] provides two main components:
+
+- **KEDA operator** allows end-users to scale workloads in/out from 0 to N instances with support for Kubernetes Deployments, Jobs, StatefulSets or any custom resource that defines `/scale` subresource.
+- **Metrics server** exposes external metrics to Horizontal Pod Autoscaler (HPA) in Kubernetes for autoscaling purposes such as messages in a Kafka topic, or number of events in an Azure event hub. Due to upstream limitations, KEDA must be the only installed metric adapter.
+
+![Diagram that shows the architecture of K E D A and how it extends Kubernetes instead of re-inventing the wheel.](./media/keda/architecture.png)
+
+Learn more about how KEDA works in the [official KEDA documentation][keda-architecture].
+
+## Installation and version
+
+KEDA can be added to your Azure Kubernetes Service (AKS) cluster by enabling the KEDA add-on using an [ARM template][keda-arm].
+
+The KEDA add-on provides a fully supported installation of KEDA that is integrated with AKS.
+
+[!INCLUDE [Current version callout](./includes/keda/current-version-callout.md)]
+
+## Capabilities and features
+
+KEDA provides the following capabilities and features:
+
+- Build sustainable and cost-efficient applications with scale-to-zero
+- Scale application workloads to meet demand using [a rich catalog of 50+ KEDA scalers][keda-scalers]
+- Autoscale applications with `ScaledObjects`, such as Deployments, StatefulSets or any custom resource that defines `/scale` subresource
+- Autoscale job-like workloads with `ScaledJobs`
+- Use production-grade security by decoupling autoscaling authentication from workloads
+- Bring-your-own external scaler to use tailor-made autoscaling decisions
+
+## Add-on limitations
+
+The KEDA AKS add-on has the following limitations:
+
+* KEDA's [HTTP add-on (preview)][keda-http-add-on] to scale HTTP workloads isn't installed with the extension, but can be deployed separately.
+* KEDA's [external scaler for Azure Cosmos DB][keda-cosmos-db-scaler] to scale based on Azure Cosmos DB change feed isn't installed with the extension, but can be deployed separately.
+* Only one metric server is allowed in the Kubernetes cluster. Because of that the KEDA add-on should be the only metrics server inside the cluster.
+    * Multiple KEDA installations aren't supported
+* Managed identity isn't supported.
+
+For general KEDA questions, we recommend [visiting the FAQ overview][keda-faq].
+
+## Next steps
+
+* [Enable the KEDA add-on with an ARM template][keda-arm]
+* [Autoscale a .NET Core worker processing Azure Service Bus Queue messages][keda-sample]
+
+<!-- LINKS - internal -->
+[keda-azure-cli]: keda-deploy-addon-az-cli.md
+[keda-arm]: keda-deploy-add-on-arm.md
+
+<!-- LINKS - external -->
+[keda]: https://keda.sh/
+[keda-architecture]: https://keda.sh/docs/latest/concepts/
+[keda-faq]: https://keda.sh/docs/latest/faq/
+[keda-sample]: https://github.com/kedacore/sample-dotnet-worker-servicebus-queue
+[keda-scalers]: https://keda.sh/docs/scalers/
+[keda-http-add-on]: https://github.com/kedacore/http-add-on
+[keda-cosmos-db-scaler]: https://github.com/kedacore/external-scaler-azure-cosmos-db

articles/aks/keda.md renamed to articles/aks/keda-deploy-add-on-arm.md

@@ -1,29 +1,21 @@
 ---
-title: KEDA add-on on Azure Kubernetes Service (AKS) (Preview)
-description: Use the KEDA add-on to deploy a managed KEDA instance on Azure Kubernetes Service (AKS).
+title: Deploy the Kubernetes Event-driven Autoscaling (KEDA) add-on by using an ARM template
+description: Use an ARM template to deploy the Kubernetes Event-driven Autoscaling (KEDA) add-on to Azure Kubernetes Service (AKS).
 services: container-service
 author: jahabibi
 ms.topic: article
-ms.custom: event-tier1-build-2022
-ms.date: 05/24/2021
+ms.date: 05/24/2022
 ms.author: jahabibi
 ---
 
-# Simplified application autoscaling with Kubernetes Event-driven Autoscaling (KEDA) add-on (Preview)
+# Deploy the Kubernetes Event-driven Autoscaling (KEDA) add-on by using ARM template
 
-Kubernetes Event-driven Autoscaling (KEDA) is a single-purpose and lightweight component that strives to make application autoscaling simple and is a CNCF Incubation project.
+This article shows you how to deploy the Kubernetes Event-driven Autoscaling (KEDA) add-on to Azure Kubernetes Service (AKS) by using an [ARM](../azure-resource-manager/templates/index.yml) template.
 
-The KEDA add-on makes it even easier by deploying a managed KEDA installation, providing you with [a rich catalog of 40+ KEDA scalers](https://keda.sh/docs/latest/scalers/) that you can scale your applications with on your Azure Kubernetes Services (AKS) cluster.
+[!INCLUDE [Current version callout](./includes/keda/current-version-callout.md)]
 
 [!INCLUDE [preview features callout](./includes/preview/preview-callout.md)]
 
-## KEDA add-on overview
-
-[KEDA][keda] provides two main components:
-
-- **KEDA operator** allows end-users to scale workloads in/out from 0 to N instances with support for Kubernetes Deployments, Jobs, StatefulSets or any custom resource that defines `/scale` subresource.
-- **Metrics server** exposes external metrics to HPA in Kubernetes for autoscaling purposes such as messages in a Kafka topic, or number of events in an Azure event hub. Due to upstream limitations, this must be the only installed metric adapter.
-
 ## Prerequisites
 
 > [!NOTE]
@@ -134,9 +126,11 @@ The following snippet is a sample deployment that creates a cluster with KEDA en
 }
 ```
 
-## Use KEDA
+## Start scaling apps with KEDA
 
-KEDA scaling will only work once a custom resource definition has been defined (CRD). To learn more about KEDA CRDs, follow the official [KEDA documentation][keda-scalers] to define your scaler.
+Now that KEDA is installed, you can start autoscaling your apps with KEDA by using its custom resource definition has been defined (CRD).
+
+To learn more about KEDA CRDs, follow the official [KEDA documentation][keda-scalers] to define your scaler.
 
 ## Clean Up
 
@@ -145,6 +139,9 @@ To remove the resource group, and all related resources, use the [az group delet
 ```azurecli
 az group delete --name MyResourceGroup
 ```
+## Next steps
+
+This article showed you how to install the KEDA add-on on an AKS cluster, and then verify that it's installed and running. With the KEDA add-on installed on your cluster, you can [deploy a sample application][keda-sample] to start scaling apps
 
 <!-- LINKS - internal -->
 [az-aks-create]: /cli/azure/aks#az-aks-create
@@ -157,3 +154,4 @@ az group delete --name MyResourceGroup
 [kubectl]: https://kubernetes.io/docs/user-guide/kubectl
 [keda]: https://keda.sh/
 [keda-scalers]: https://keda.sh/docs/scalers/
+[keda-sample]: https://github.com/kedacore/sample-dotnet-worker-servicebus-queue

articles/aks/keda-integrations.md

@@ -0,0 +1,63 @@
+---
+title: Integrations with Kubernetes Event-driven Autoscaling (KEDA) on Azure Kubernetes Service (AKS) (Preview)
+description: Integrations with Kubernetes Event-driven Autoscaling (KEDA) on Azure Kubernetes Service (AKS) (Preview).
+services: container-service
+author: tomkerkhove
+ms.topic: article
+ms.date: 05/24/2022
+ms.author: tomkerkhove
+---
+
+# Integrations with Kubernetes Event-driven Autoscaling (KEDA) on Azure Kubernetes Service (AKS) (Preview)
+
+The Kubernetes Event-driven Autoscaling (KEDA) add-on integrates with features provided by Azure and open source projects.
+
+[!INCLUDE [preview features callout](./includes/preview/preview-callout.md)]
+
+> [!IMPORTANT]
+> Integrations with open source projects are not covered by the [AKS support policy][aks-support-policy].
+
+## Observe your autoscaling with Kubernetes events
+
+KEDA automatically emits Kubernetes events allowing customers to operate their application autoscaling.
+
+To learn about the available metrics, we recommend reading the [KEDA documentation][keda-event-docs].
+
+## Scalers for Azure services
+
+KEDA can integrate with various tools and services through [a rich catalog of 50+ KEDA scalers][keda-scalers]. It supports leading cloud platforms (such as Azure) and open-source technologies such as Redis and Kafka.
+
+It leverages the following scalers for Azure services:
+
+- [Azure Application Insights](https://keda.sh/docs/latest/scalers/azure-app-insights/)
+- [Azure Blob Storage](https://keda.sh/docs/latest/scalers/azure-storage-blob/)
+- [Azure Data Explorer](https://keda.sh/docs/latest/scalers/azure-data-explorer/)
+- [Azure Event Hubs](https://keda.sh/docs/latest/scalers/azure-event-hub/)
+- [Azure Log Analytics](https://keda.sh/docs/latest/scalers/azure-log-analytics/)
+- [Azure Monitor](https://keda.sh/docs/latest/scalers/azure-monitor/)
+- [Azure Pipelines](https://keda.sh/docs/latest/scalers/azure-pipelines/)
+- [Azure Service Bus](https://keda.sh/docs/latest/scalers/azure-service-bus/)
+- [Azure Storage Queue](https://keda.sh/docs/latest/scalers/azure-storage-queue/)
+
+Next to the built-in scalers, you can install external scalers yourself to autoscale on other Azure services:
+
+- [Azure Cosmos DB (Change feed)](https://github.com/kedacore/external-scaler-azure-cosmos-db)
+
+However, these external scalers aren't supported as part of the add-on and rely on community support.
+
+## Next steps
+
+* [Enable the KEDA add-on with an ARM template][keda-arm]
+* [Autoscale a .NET Core worker processing Azure Service Bus Queue message][keda-sample]
+
+<!-- LINKS - internal -->
+[aks-support-policy]: support-policies.md
+[azure-monitor]: ../azure-monitor/overview.md
+[azure-monitor-container-insights]: ../azure-monitor/containers/container-insights-onboard.md
+[keda-arm]: keda-deploy-add-on-arm.md
+
+<!-- LINKS - external -->
+[keda-scalers]: https://keda.sh/docs/scalers/
+[keda-metrics]: https://keda.sh/docs/latest/operate/prometheus/
+[keda-event-docs]: https://keda.sh/docs/latest/operate/kubernetes-events/
+[keda-sample]: https://github.com/kedacore/sample-dotnet-worker-servicebus-queue

articles/aks/media/keda/architecture.png

@@ -74,7 +74,7 @@ If you want to use manually labeled data, you'll also have to upload the *.label
 
 When you [train your model](https://formrecognizer.appliedai.azure.com/studio/custommodel/projects) with labeled data, the model uses supervised learning to extract values of interest, using the labeled forms you provide. Labeled data results in better-performing models and can produce models that work with complex forms or forms containing values without keys.
 
-Form Recognizer uses the [prebuilt-layout model](https://westus.dev.cognitive.microsoft.com/docs/services/form-recognizer-api-v3-0-preview-2/operations/AnalyzeDocument) API to learn the expected sizes and positions of printed and handwritten text elements and extract tables. Then it uses user-specified labels to learn the key/value associations and tables in the documents. We recommend that you use five manually labeled forms of the same type (same structure) to get started with training a new model. Then, add more labeled data, as needed, to improve the model accuracy. Form Recognizer enables training a model to extract key-value pairs and tables using supervised learning capabilities.
+Form Recognizer uses the [prebuilt-layout model](https://westus.dev.cognitive.microsoft.com/docs/services/form-recognizer-api-v3-0-preview-2/operations/AnalyzeDocument) API to learn the expected sizes and positions of typeface and handwritten text elements and extract tables. Then it uses user-specified labels to learn the key/value associations and tables in the documents. We recommend that you use five manually labeled forms of the same type (same structure) to get started with training a new model. Then, add more labeled data, as needed, to improve the model accuracy. Form Recognizer enables training a model to extract key-value pairs and tables using supervised learning capabilities.
 
 ### [Form Recognizer Studio](#tab/studio)
 

articles/applied-ai-services/form-recognizer/compose-custom-models-preview.md

@@ -74,7 +74,7 @@ You [train your model](./quickstarts/try-sdk-rest-api.md#train-a-custom-model)
 
 When you train with labeled data, the model uses supervised learning to extract values of interest, using the labeled forms you provide. Labeled data results in better-performing models and can produce models that work with complex forms or forms containing values without keys.
 
-Form Recognizer uses the [Layout](concept-layout.md) API to learn the expected sizes and positions of printed and handwritten text elements and extract tables. Then it uses user-specified labels to learn the key/value associations and tables in the documents. We recommend that you use five manually labeled forms of the same type (same structure) to get started when training a new model and add more labeled data as needed to improve the model accuracy. Form Recognizer enables training a model to extract key value pairs and tables using supervised learning capabilities.
+Form Recognizer uses the [Layout](concept-layout.md) API to learn the expected sizes and positions of typeface and handwritten text elements and extract tables. Then it uses user-specified labels to learn the key/value associations and tables in the documents. We recommend that you use five manually labeled forms of the same type (same structure) to get started when training a new model and add more labeled data as needed to improve the model accuracy. Form Recognizer enables training a model to extract key value pairs and tables using supervised learning capabilities.
 
 [Get started with Train with labels](label-tool.md)