Merge pull request #271200 from MicrosoftDocs/repo_sync_working_branch

Confirm merge from repo_sync_working_branch to main to sync with https://github.com/MicrosoftDocs/azure-docs (branch main)

Author: v-alje

articles/advisor/advisor-reference-operational-excellence-recommendations.md

@@ -516,7 +516,15 @@ Virtual Network flow log allows you to record IP traffic flowing in a virtual ne
 
 Learn more about [Resource - UpgradeNSGToVnetFlowLog (Upgrade NSG flow logs to VNet flow logs)](https://aka.ms/vnetflowlogspreviewdocs).
 
+### Migrate Azure Front Door (classic) to Standard/Premium tier
 
+On 31 March 2027, Azure Front Door (classic) will be retired for the public cloud, and you’ll need to migrate to Front Door Standard or Premium by that date.
+
+Beginning 1 April 2025, you’ll no longer be able to create new Front Door (classic) resources via the Azure portal, Terraform, or any command line tools. However, you can continue to make modifications to existing resources until Front Door (classic) is fully retired.
+
+Azure Front Door Standard and Premium combine the capabilities of static and dynamic content delivery with turnkey security, enhanced DevOps experiences, simplified pricing, and better Azure integrations
+
+Learn more about [Azure Front Door (classic) will be retired on 31 March 2027](https://azure.microsoft.com/updates/azure-front-door-classic-will-be-retired-on-31-march-2027/).
 
 
 

articles/defender-for-cloud/create-custom-recommendations.md

@@ -87,7 +87,7 @@ You can create custom recommendations and standards in Defender for cloud by cre
 
 Here's how you do that:
 
-1. Create one or more policy definitions in the [Azure Policy portal](../governance/policy/tutorials/create-custom-policy-definition.md), or [programatically](../governance/policy/how-to/programmatically-create.md).
+1. Create one or more policy definitions in the [Azure Policy portal](../governance/policy/tutorials/create-custom-policy-definition.md), or [programmatically](../governance/policy/how-to/programmatically-create.md).
 1. [Create a policy initiative](../governance/policy/concepts/initiative-definition-structure.md) that contains the custom policy definitions.
 
 ### Onboard the initiative as a custom standard (legacy)

articles/defender-for-cloud/defender-for-cloud-glossary.md

@@ -151,7 +151,7 @@ Amazon Elastic Kubernetes Service, Amazon's managed service for running Kubernet
 
 ### **eBPF**
 
-Extended Berkley Packet Filter [What is eBPF?](https://ebpf.io/)
+Extended Berkeley Packet Filter [What is eBPF?](https://ebpf.io/)
 
 ## F
 

articles/defender-for-cloud/enable-pull-request-annotations.md

@@ -141,7 +141,7 @@ All annotations on your pull requests will be displayed from now on based on you
 
 **To enable pull request annotations for my Projects and Organizations in Azure DevOps**:
 
-You can do this programatically by calling the Update Azure DevOps Resource API exposed the Microsoft. Security
+You can do this programmatically by calling the Update Azure DevOps Resource API exposed the Microsoft. Security
 Resource Provider.
 
 API Info:

articles/machine-learning/concept-automl-forecasting-at-scale.md

@@ -14,9 +14,7 @@ ms.date: 08/01/2023
 show_latex: true
 ---
 
-# Forecasting at scale: many models and distributed training (preview)
-
-[!INCLUDE [machine-learning-preview-generic-disclaimer](./includes/machine-learning-preview-generic-disclaimer.md)]
+# Forecasting at scale: many models and distributed training
 
 This article is about training forecasting models on large quantities of historical data. Instructions and examples for training forecasting models in AutoML can be found in our [set up AutoML for time series forecasting](./how-to-auto-train-forecast.md) article.
 
@@ -28,7 +26,7 @@ The many models [components](concept-component.md) in AutoML enable you to train
 
 :::image type="content" source="./media/how-to-auto-train-forecast/many-models.svg" alt-text="Diagram showing the AutoML many models workflow.":::
 
-The many models training component applies AutoML's [model sweeping and selection](concept-automl-forecasting-sweeping.md) independently to each store in this example. This model independence aids scalability and can benefit model accuracy especially when the stores have diverging sales dynamics. However, a single model approach may yield more accurate forecasts when there are common sales dynamics. See the [distributed DNN training](#distributed-dnn-training) section for more details on that case.
+The many models training component applies AutoML's [model sweeping and selection](concept-automl-forecasting-sweeping.md) independently to each store in this example. This model independence aids scalability and can benefit model accuracy especially when the stores have diverging sales dynamics. However, a single model approach may yield more accurate forecasts when there are common sales dynamics. See the [distributed DNN training](#distributed-dnn-training-preview) section for more details on that case.
 
 You can configure the data partitioning, the [AutoML settings](how-to-auto-train-forecast.md#configure-experiment) for the models, and the degree of parallelism for many models training jobs. For examples, see our guide section on [many models components](how-to-auto-train-forecast.md#forecasting-at-scale-many-models).        
 
@@ -53,7 +51,9 @@ AutoML supports the following features for hierarchical time series (HTS):
 HTS components in AutoML are built on top of [many models](#many-models), so HTS shares the scalable properties of many models. 
 For examples, see our guide section on [HTS components](how-to-auto-train-forecast.md#forecasting-at-scale-hierarchical-time-series).
 
-## Distributed DNN training
+## Distributed DNN training (preview)
+
+[!INCLUDE [machine-learning-preview-generic-disclaimer](./includes/machine-learning-preview-generic-disclaimer.md)]
 
 Data scenarios featuring large amounts of historical observations and/or large numbers of related time series may benefit from a scalable, single model approach. Accordingly, **AutoML supports distributed training and model search on temporal convolutional network (TCN) models**, which are a type of deep neural network (DNN) for time series data. For more information on AutoML's TCN model class, see our [DNN article](concept-automl-forecasting-deep-learning.md).
 

articles/machine-learning/how-to-auto-train-forecast.md

@@ -1585,7 +1585,7 @@ Also see the [demand forecasting with hierarchical time series notebook](https:/
 
 ## Forecasting at scale: distributed DNN training
 
-* To learn how distributed training works for forecasting tasks, see our [forecasting at scale article](concept-automl-forecasting-at-scale.md#distributed-dnn-training). 
+* To learn how distributed training works for forecasting tasks, see our [forecasting at scale article](concept-automl-forecasting-at-scale.md#distributed-dnn-training-preview). 
 * See our [setup distributed training for tabular data](how-to-configure-auto-train.md#automl-at-scale-distributed-training) article section for code samples. 
 
 ## Example notebooks

articles/machine-learning/how-to-configure-auto-train.md

@@ -698,7 +698,7 @@ limits:
 
 ### Distributed training for forecasting
 
-To learn how distributed training works for forecasting tasks, see our [forecasting at scale](concept-automl-forecasting-at-scale.md#distributed-dnn-training) article. To use distributed training for forecasting, you need to set the `training_mode`, `enable_dnn_training`, `max_nodes`, and optionally the `max_concurrent_trials` properties of the job object.
+To learn how distributed training works for forecasting tasks, see our [forecasting at scale](concept-automl-forecasting-at-scale.md#distributed-dnn-training-preview) article. To use distributed training for forecasting, you need to set the `training_mode`, `enable_dnn_training`, `max_nodes`, and optionally the `max_concurrent_trials` properties of the job object.
 
 Property | Description
 -- | --