Merge pull request #6174 from MicrosoftDocs/main

Auto Publish – main to live - 2025-07-23 05:05 UTC

Author: learn-build-service-prod[bot]

.openpublishing.redirection.json

@@ -489,6 +489,26 @@
       "source_path": "articles/ai-foundry/model-inference/index.yml",
       "redirect_url": "../foundry-models/index",
       "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/machine-learning/concept-package-models.md",
+      "redirect_url": "/azure/machine-learning/concept-endpoints",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/machine-learning/how-to-package-models-moe.md",
+      "redirect_url": "/azure/machine-learning/concept-endpoints",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/machine-learning/how-to-package-models-app-service.md",
+      "redirect_url": "/azure/machine-learning/concept-endpoints",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/machine-learning/how-to-package-models.md",
+      "redirect_url": "/azure/machine-learning/concept-endpoints",
+      "redirect_document_id": false
     }
   ]
 }

articles/ai-foundry/how-to/configure-private-link.md

@@ -48,8 +48,6 @@ You get several hub default resources in your resource group. You need to config
     > [!IMPORTANT]
     > We don't recommend using the 172.17.0.0/16 IP address range for your VNet. This is the default subnet range used by the Docker bridge network on-premises.
 
-* Disable network policies for private endpoints before adding the private endpoint.
-
 :::zone pivot="fdp-project"
 
 ## Create a Foundry project that uses a private endpoint

articles/ai-services/content-safety/overview.md

@@ -153,7 +153,7 @@ For more information, see [Language support](/azure/ai-services/content-safety/l
 
 To use the Content Safety APIs, you must create your Azure AI Content Safety resource in a supported region. Currently, the Content Safety features are available in the following Azure regions with different API versions: 
 
-| Region              | Custom Category    | Groundedness       | Image | Multimodal(Image with Tex) | Incident Response | Prompt Shield | Protected Material (Text) | Protected Material (Code) | Text | 
+| Region              | Custom Category (standard)   | Groundedness       | Image | Multimodal(Image with Tex) | Custom Category (rapid) | Prompt Shield | Protected Material (Text) | Protected Material (Code) | Text | 
 |--------------------|--------------------|--------------------|-------|-----------------------------|-------------------|---------------|---------------------------|---------------------------|------|
 | Australia East     | ✅                  |                    | ✅    |                            | ✅               | ✅             | ✅                     | ✅                        | ✅    | 
 | Canada East          |                    |                    | ✅    |                            | ✅               | ✅             | ✅                     | ✅                        | ✅    | 

articles/ai-services/language-service/conversational-language-understanding/concepts/entity-components.md

@@ -6,24 +6,24 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: conceptual
-ms.date: 06/04/2025
+ms.date: 07/22/2025
 ms.author: lajanuar
 ms.custom: language-service-clu
 ---
 
 # Entity components
 
-In conversational language understanding, entities are relevant pieces of information that are extracted from your utterances. An entity can be extracted by different methods. They can be learned through context, matched from a list, or detected by a prebuilt recognized entity. Every entity in your project is composed of one or more of these methods, which are defined as your entity's components. 
+In conversational language understanding, entities are relevant pieces of information that are extracted from your utterances. You can extract an entity using several different methods. Entities can be detected through context, matched from a list, or detected by a prebuilt recognized entity. Every entity in your project is composed of one or more of these methods, which are defined as your entity's components.
 
-When an entity is defined by more than one component, their predictions can overlap. You can determine the behavior of an entity prediction when its components overlap by using a fixed set of options in the *entity options*.
+When more than one component defines an entity, predictions can overlap. You can determine the behavior of an entity prediction when its components overlap by using a fixed set of options in the *entity options*.
 
 ## Component types
 
 An entity component determines a way that you can extract the entity. An entity can contain one component, which determines the only method to be used to extract the entity. An entity can also contain multiple components to expand the ways in which the entity is defined and extracted.
 
 ### Learned component
 
-The learned component uses the entity tags you label your utterances with to train a machine-learned model. The model learns to predict where the entity is based on the context within the utterance. Your labels provide examples of where the entity is expected to be present in an utterance, based on the meaning of the words around it and as the words that were labeled. 
+The learned component uses the entity tags you label your utterances with to train a machine-learned model. The model learns to predict where the entity is based on the context within the utterance. Your labels provide examples of where the entity is expected to be present in an utterance. This determination is based on the meaning of the words around it and as the words that were labeled.
 
 This component is only defined if you add labels by tagging utterances for the entity. If you don't tag any utterances with the entity, it doesn't have a learned component.
 
@@ -53,13 +53,13 @@ In multilingual projects, you can specify a different expression for each langua
 
 ## Entity options
 
-When multiple components are defined for an entity, their predictions might overlap. When an overlap occurs, each entity's final prediction is determined by one of the following options.
+If multiple components define an entity, their predictions may overlap. When overlap happens, one of the following options determines each entity's final prediction:
 
 ### Combine components
 
 Combine components as one entity when they overlap by taking the union of all the components.
 
-Use this option to combine all components when they overlap. When components are combined, you get all the extra information that's tied to a list or prebuilt component when they're present.
+Use this option to combine all components when they overlap. When components are combined, you get all the extra information associated with a list or prebuilt component if present.
 
 #### Example
 
@@ -71,7 +71,7 @@ By using combined components, the entity returns with the full context as "Prose
 
 :::image type="content" source="../media/union-overlap-example-1-part-2.svg" alt-text="Screenshot that shows the result of a combined component." lightbox="../media/union-overlap-example-1-part-2.svg":::
 
-Suppose you had the same utterance, but only "OS 9" was predicted by the learned component:
+Suppose you had the same utterance, but only "OS 9" predicts the learned component:
 
 :::image type="content" source="../media/union-overlap-example-2.svg" alt-text="Screenshot that shows an utterance with O S 9 predicted by the learned component." lightbox="../media/union-overlap-example-2.svg":::
 
@@ -95,7 +95,7 @@ When you don't combine components, the entity returns twice:
 
 ### Required components
 
-Sometimes an entity can be defined by multiple components but requires one or more of them to be present. Every component can be set as *required*, which means the entity *won't* be returned if that component wasn't present. For example, if you have an entity with a list component and a required learned component, it's guaranteed that any returned entity includes a learned component. If it doesn't, the entity isn't returned.
+Sometimes, you can define an entity using multiple components, but the entity requires at least one or more of them to be present. You can mark any component as *required*, which means the system *doesn't* return the entity unless that component is present. For example, if an entity has a list component and a *required* learned component, the system guarantees that any returned entity includes a learned component. If an entity doesn't have the required component, the system doesn't return it.
 
 Required components are most frequently used with learned components because they can restrict the other component types to a specific context, which is commonly associated to *roles*. You can also require all components to make sure that every component is present for an entity.
 
@@ -120,8 +120,9 @@ Other times, you might be interested in extracting an entity through context, su
 When you don't combine components, you allow every component to act as an independent entity extractor. One way of using this option is to separate the entities extracted from a list to the ones extracted through the learned or prebuilt components to handle and treat them differently.
 
 > [!NOTE]
-> Previously during the public preview of the service, there were four available options: **Longest overlap**, **Exact overlap**, **Union overlap**, and **Return all separately**. **Longest overlap** and **Exact overlap** are deprecated and are only supported for projects that previously had those options selected. **Union overlap** has been renamed to **Combine components**, while **Return all separately** has been renamed to **Do not combine components**.
+> Previously during the public preview of the service, there were four available options: **Longest overlap**, **Exact overlap**, **Union overlap**, and **Return all separately**. **Longest overlap** and **Exact overlap** are deprecated and are only supported for projects that previously had those options selected. **Union overlap** is renamed to **Combine components**, while **Return all separately** is renamed to **Do not combine components**.
 
 ## Related content
 
-- [Supported prebuilt components](../prebuilt-component-reference.md)
+[Supported prebuilt components](../prebuilt-component-reference.md)
+

articles/ai-services/language-service/toc.yml

@@ -839,8 +839,14 @@ items:
       items:
       - name: C#
         items:
-        - name: Text analysis
-          href: /dotnet/api/overview/azure/ai.textanalytics-readme?view=azure-dotnet-preview&preserve-view=true
+        - name: Language Text
+          href: /dotnet/api/overview/azure/ai.language.text-readme?view=azure-dotnet-preview&preserve-view=true
+        - name: Conversational Language Understanding
+          href: /dotnet/api/overview/azure/ai.language.conversations-readme?view=azure-dotnet-preview&preserve-view=true
+        - name: Conversations Authoring
+          href: /dotnet/api/overview/azure/ai.language.conversations.authoring-readme?view=azure-dotnet-preview&preserve-view=true
+        - name: Text Authoring
+          href: /dotnet/api/overview/azure/ai.language.text.authoring-readme?view=azure-dotnet-preview&preserve-view=true
         - name: Custom question answering
           href: /dotnet/api/overview/azure/ai.language.questionanswering-readme?view=azure-dotnet-preview&preserve-view=true
       - name: Python

articles/machine-learning/concept-endpoints.md

@@ -124,7 +124,6 @@ The following table shows a summary of the different features available to stand
 | Deployment types              | Models                                          | Models                                          | Models and Pipeline components                |
 | MLflow model deployment       | No, only specific models in the catalog         | Yes                                             | Yes                                           |
 | Custom model deployment       | No, only specific models in the catalog         | Yes, with scoring script                        | Yes, with scoring script                      |
-| Model package deployment  <sup>2</sup>    | Built-in | Yes (preview)                                   | No                                            |
 | Inference server <sup>3</sup> | Azure AI Model Inference API                    | - Azure Machine Learning Inferencing Server<br /> - Triton<br /> - Custom (using BYOC)  | Batch Inference        |
 | Compute resource consumed     | None (serverless)                               | Instances or granular resources                 | Cluster instances                             |
 | Compute type                  | None (serverless)                               | Managed compute and Kubernetes                  | Managed compute and Kubernetes                |
@@ -135,13 +134,12 @@ The following table shows a summary of the different features available to stand
 | Cost basis<sup>5</sup>        | Per token                                      | Per deployment: compute instances running       | Per job: compute instanced consumed in the job  (capped to the maximum number of instances of the cluster) |
 | Local testing of deployments  | No                                              | Yes                                             | No                                            |
 
-<sup>2</sup> Deploying MLflow models to endpoints without outbound internet connectivity or private networks requires [packaging the model](concept-package-models.md) first.
 
-<sup>3</sup> *Inference server* refers to the serving technology that takes requests, processes them, and creates responses. The inference server also dictates the format of the input and the expected outputs.
+<sup>2</sup> *Inference server* refers to the serving technology that takes requests, processes them, and creates responses. The inference server also dictates the format of the input and the expected outputs.
 
-<sup>4</sup> *Autoscaling* is the ability to dynamically scale up or scale down the deployment's allocated resources based on its load. Online and batch deployments use different strategies for autoscaling. While online deployments scale up and down based on the resource utilization (like CPU, memory, requests, etc.), batch endpoints scale up or down based on the number of jobs created.
+<sup>3</sup> *Autoscaling* is the ability to dynamically scale up or scale down the deployment's allocated resources based on its load. Online and batch deployments use different strategies for autoscaling. While online deployments scale up and down based on the resource utilization (like CPU, memory, requests, etc.), batch endpoints scale up or down based on the number of jobs created.
 
-<sup>5</sup> Both online and batch deployments charge by the resources consumed. In online deployments, resources are provisioned at deployment time. In batch deployment, resources aren't consumed at deployment time but at the time that the job runs. Hence, there's no cost associated with the batch deployment itself. Likewise, queued jobs don't consume resources either.
+<sup>4</sup> Both online and batch deployments charge by the resources consumed. In online deployments, resources are provisioned at deployment time. In batch deployment, resources aren't consumed at deployment time but at the time that the job runs. Hence, there's no cost associated with the batch deployment itself. Likewise, queued jobs don't consume resources either.
 
 ## Developer interfaces