Merge pull request #4252 from MicrosoftDocs/main

Taojunshen · web-flow · commit ec2f2711b3b6 · 2025-04-23T01:01:31.000+08:00
4/22/2025 AM Publish
diff --git a/articles/ai-foundry/toc.yml b/articles/ai-foundry/toc.yml
@@ -157,10 +157,14 @@ items:
       items:
       - name: Batch
         href: ../ai-services/openai/how-to/batch.md?context=/azure/ai-foundry/context/context
-        displayName: OpenAI, global batch, globalbatch, chat, chat completions
+        displayName: OpenAI, global batch, globalbatch, chat, chat completions 
       - name: Reasoning models
         href: ../ai-services/openai/how-to/reasoning.md?context=/azure/ai-foundry/context/context
         displayName: OpenAI, o1, o1-mini, o3-mini, reasoning effort
+      - name: Responses API
+        href: ../ai-services/openai/how-to/responses.md?context=/azure/ai-foundry/context/context
+      - name: Computer use 
+        href: ../ai-services/openai/how-to/computer-use.md?context=/azure/ai-foundry/context/context
       - name: Function calling
         href: ../ai-services/openai/how-to/function-calling.md?context=/azure/ai-foundry/context/context
         displayName: OpenAI 
@@ -195,9 +199,32 @@ items:
       items:
       - name: Fine-tune Azure OpenAI models
         href: ../ai-services/openai/how-to/fine-tuning.md?context=/azure/ai-foundry/context/context
-        displayname: vision fine-tuning, DPO, direct preference optimization
+        displayName: finetuning, fine-tuning 
       - name: When to use Azure OpenAI fine-tuning
         href: ../ai-services/openai/concepts/fine-tuning-considerations.md?context=/azure/ai-foundry/context/context
+        displayName: finetuning, fine-tuning 
+      - name: Deploy your Azure OpenAI fine-tuned model
+        href: ../ai-services/openai/how-to/fine-tuning-deploy.md?context=/azure/ai-foundry/context/context
+        displayName: finetuning, fine-tuning         
+      - name: Vision fine-tuning
+        href: ../ai-services/openai/how-to/fine-tuning-vision.md?context=/azure/ai-foundry/context/context
+        displayName: finetuning, fine-tuning 
+      - name: Preference fine-tuning
+        href: ../ai-services/openai/how-to/fine-tuning-direct-preference-optimization.md?context=/azure/ai-foundry/context/context
+        displayName: finetuning, fine-tuning 
+      - name: Safety evaluation
+        href: ../ai-services/openai/how-to/fine-tuning-safety-evaluation.md?context=/azure/ai-foundry/context/context
+        displayName: finetuning, fine-tuning 
+      - name: Tool calling
+        href: ../ai-services/openai/how-to/fine-tuning-functions.md?context=/azure/ai-foundry/context/context
+        displayName: finetuning, fine-tuning         
+      - name: Weights & Biases integration (preview)
+        href: ../ai-services/openai/how-to/weights-and-biases-integration.md?context=/azure/ai-foundry/context/context
+        displayName: finetuning, fine-tuning 
+      - name: Troubleshooting guidance
+        href: ../ai-services/openai/how-to/fine-tuning-troubleshoot.md?context=/azure/ai-foundry/context/context
+        displayName: finetuning, fine-tuning 
+
   - name: Content Safety
     items: 
     - name: What is Azure AI Content Safety service?
@@ -360,7 +387,34 @@ items:
       - name: Fine-tune models deployed via managed compute
         href: how-to/fine-tune-managed-compute.md
     - name: Fine-tune Azure OpenAI models
-      href: ../ai-services/openai/how-to/fine-tuning.md?context=/azure/ai-foundry/context/context
+      items:
+      - name: Fine-tune Azure OpenAI models
+        href: ../ai-services/openai/how-to/fine-tuning.md?context=/azure/ai-foundry/context/context
+        displayName: finetuning, fine-tuning 
+      - name: When to use Azure OpenAI fine-tuning
+        href: ../ai-services/openai/concepts/fine-tuning-considerations.md?context=/azure/ai-foundry/context/context
+        displayName: finetuning, fine-tuning 
+      - name: Deploy your Azure OpenAI fine-tuned model
+        href: ../ai-services/openai/how-to/fine-tuning-deploy.md?context=/azure/ai-foundry/context/context
+        displayName: finetuning, fine-tuning         
+      - name: Vision fine-tuning
+        href: ../ai-services/openai/how-to/fine-tuning-vision.md?context=/azure/ai-foundry/context/context
+        displayName: finetuning, fine-tuning 
+      - name: Preference fine-tuning
+        href: ../ai-services/openai/how-to/fine-tuning-direct-preference-optimization.md?context=/azure/ai-foundry/context/context
+        displayName: finetuning, fine-tuning 
+      - name: Safety evaluation
+        href: ../ai-services/openai/how-to/fine-tuning-safety-evaluation.md?context=/azure/ai-foundry/context/context
+        displayName: finetuning, fine-tuning 
+      - name: Tool calling
+        href: ../ai-services/openai/how-to/fine-tuning-functions.md?context=/azure/ai-foundry/context/context
+        displayName: finetuning, fine-tuning         
+      - name: Weights & Biases integration (preview)
+        href: ../ai-services/openai/how-to/weights-and-biases-integration.md?context=/azure/ai-foundry/context/context
+        displayName: finetuning, fine-tuning 
+      - name: Troubleshooting guidance
+        href: ../ai-services/openai/how-to/fine-tuning-troubleshoot.md?context=/azure/ai-foundry/context/context
+        displayName: finetuning, fine-tuning 
     - name: Distillation
       href: concepts/concept-model-distillation.md
   - name: Tracing
diff --git a/articles/ai-services/openai/how-to/function-calling.md b/articles/ai-services/openai/how-to/function-calling.md
@@ -43,14 +43,14 @@ At a high level you can break down working with functions into three steps:
 * `gpt-4.1` (`2025-04-14`)
 * `gpt-4.1-nano` (`2025-04-14`)
 * `gpt-4.1-mini` (`2025-04-14`)
-* `o4-mini` (`2025-04-16`)
-* `o3` (`2025-04-16`)
 
 Support for parallel function was first added in API version [`2023-12-01-preview`](https://github.com/Azure/azure-rest-api-specs/blob/main/specification/cognitiveservices/data-plane/AzureOpenAI/inference/preview/2023-12-01-preview/inference.json)
 
 ### Basic function calling with tools
 
 * All the models that support parallel function calling
+* `o4-mini` (`2025-04-16`)
+* `o3` (`2025-04-16`)
 * `o3-mini` (`2025-01-31`)
 * `o1` (`2024-12-17`)
 * `gpt-4` (`0613`)
diff --git a/articles/ai-services/openai/how-to/reasoning.md b/articles/ai-services/openai/how-to/reasoning.md
@@ -48,7 +48,7 @@ Azure OpenAI `o-series` models are designed to tackle reasoning and problem-solv
 | Chat Completions API | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
 | Responses API | ✅ | ✅  | - | - | - | - |
 | Functions/Tools | ✅ | ✅ | ✅  | ✅  |  - | - |
-| Parallel Tool Calls | ✅ | ✅ | -  | -  |  - | - |
+| Parallel Tool Calls | - | - | -  | -  |  - | - |
 | `max_completion_tokens`<sup>*</sup> | ✅ | ✅ |✅ |✅ |✅ | ✅ |
 | System Messages<sup>**</sup> | ✅ | ✅ | ✅ | ✅ | - | - |
 | [Reasoning summary](#reasoning-summary) <sup>***</sup> | ✅ | ✅ | -  | -  |  - | - |
diff --git a/articles/ai-services/speech-service/includes/spx-setup.md b/articles/ai-services/speech-service/includes/spx-setup.md
@@ -80,7 +80,7 @@ Enter `spx` or `spx help` to see help for the Speech CLI.
 
 #### [Docker (Windows, Linux, macOS)](#tab/dockerinstall)
 
-The following example pulls a public container image from Docker Hub. We recommend that you authenticate with your Docker Hub account (`docker login`) first instead of making an anonymous pull request. To improve reliability when you're using public content, import and manage the image in a private Azure container registry. [Learn more about working with public images](/azure/container-registry/buffer-gate-public-content).
+The following example pulls a public container image from Docker Hub. We recommend that you authenticate with your Docker Hub account (`docker login`) first instead of making an anonymous pull request. To improve reliability when you're using public content, import and manage the image in a private Azure Container Registry. [Learn more about working with public images](/azure/container-registry/buffer-gate-public-content).
 
 Follow these steps to install the Speech CLI in a Docker container:
 
diff --git a/articles/machine-learning/concept-endpoints-online-auth.md b/articles/machine-learning/concept-endpoints-online-auth.md
@@ -121,7 +121,7 @@ If the endpoint identity is a SAI, the following roles are assigned to the endpo
 
 | Role | Description | Condition for automatic role assignment |
 | --- | --- | --- |
-| **AcrPull** | Allows the endpoint identity to pull images from the Azure container registry associated with the workspace | The endpoint identity is a SAI.
+| **AcrPull** | Allows the endpoint identity to pull images from the Azure Container Registry associated with the workspace | The endpoint identity is a SAI.
 | **Storage Blob Data Reader** | Allows the endpoint identity to read blobs from the default datastore of the workspace | The endpoint identity is a SAI.
 | **AzureML Metrics Writer (preview)** | Allows the endpoint identity to write metrics to the workspace | The endpoint identity is a SAI.
 | **Azure Machine Learning Workspace Connection Secrets Reader** | Allows the endpoint identity to read secrets from workspace connections | The endpoint identity is a SAI and the endpoint creation has a flag to enforce access to the default secret stores. The user identity that creates the endpoint also has permission to read secrets from workspace connections.
diff --git a/articles/machine-learning/how-to-access-azureml-behind-firewall.md b/articles/machine-learning/how-to-access-azureml-behind-firewall.md
@@ -175,7 +175,7 @@ Besides above requirements, the following outbound URLs are also required for Az
 | Outbound Endpoint| Port | Description|Training |Inference |
 |-----|-----|-----|:-----:|:-----:|
 | `*.kusto.windows.net`<br>`*.table.core.windows.net`<br>`*.queue.core.windows.net` | 443 | Required to upload system logs to Kusto. |__&check;__|__&check;__|
-| `<your ACR name>.azurecr.io`<br>`<your ACR name>.<region>.data.azurecr.io` | 443 | Azure container registry, required to pull docker images used for machine learning workloads.|__&check;__|__&check;__|
+| `<your ACR name>.azurecr.io`<br>`<your ACR name>.<region>.data.azurecr.io` | 443 | Azure Container Registry, required to pull docker images used for machine learning workloads.|__&check;__|__&check;__|
 | `<your storage account name>.blob.core.windows.net` | 443 | Azure blob storage, required to fetch machine learning project scripts, data or models, and upload job logs/outputs.|__&check;__|__&check;__|
 | `<your workspace ID>.workspace.<region>.api.azureml.ms`<br>`<region>.experiments.azureml.net`<br>`<region>.api.azureml.ms` | 443 | Azure Machine Learning service API.|__&check;__|__&check;__|
 | `pypi.org` | 443 | Python package index, to install pip packages used for training job environment initialization.|__&check;__|N/A|
diff --git a/articles/machine-learning/how-to-manage-workspace-cli.md b/articles/machine-learning/how-to-manage-workspace-cli.md
@@ -153,7 +153,7 @@ The following considerations and limitations apply to dependent resources associ
 The Azure Machine Learning workspace uses Azure Container Registry for some operations, and automatically creates a Container Registry instance when it first needs one.
 [!INCLUDE [machine-learning-delete-acr](includes/machine-learning-delete-acr.md)]
 
-To use an existing Azure container registry with an Azure Machine Learning workspace, you must [enable the admin account](/azure/container-registry/container-registry-authentication#admin-account) on the container registry.
+To use an existing Azure Container Registry with an Azure Machine Learning workspace, you must [enable the admin account](/azure/container-registry/container-registry-authentication#admin-account) on the container registry.
 
 #### Storage Account
 
diff --git a/articles/machine-learning/how-to-package-models-app-service.md b/articles/machine-learning/how-to-package-models-app-service.md
@@ -150,9 +150,9 @@ In this section, you package the previously registered MLflow model and deploy i
 
     1. Look for the environment named *heart-classifier-mlflow-package*, which is the name of the package you just created.
 
-    1. Copy the value that's in the **Azure container registry** field.
+    1. Copy the value that's in the **Azure Container Registry** field.
 
-    :::image type="content" source="./media/model-packaging/model-package-container-name.png" alt-text="A screenshot showing the section where the Azure container registry image name is displayed in Azure Machine Learning studio."::: 
+    :::image type="content" source="./media/model-packaging/model-package-container-name.png" alt-text="A screenshot showing the section where the Azure Container Registry image name is displayed in Azure Machine Learning studio."::: 
 
 
 1. Now, deploy this package in an App Service.
@@ -177,7 +177,7 @@ In this section, you package the previously registered MLflow model and deploy i
     
     1. For **Image Source**, select **Azure Container Registry**.
 
-    1. Configure the **Azure container registry options** as follows:
+    1. Configure the **Azure Container Registry options** as follows:
 
         1. For **Registry**, select the Azure Container Registry associated with the Azure Machine Learning workspace.
 
diff --git a/articles/machine-learning/how-to-prevent-data-loss-exfiltration.md b/articles/machine-learning/how-to-prevent-data-loss-exfiltration.md
@@ -159,7 +159,7 @@ Use the following steps to enable a storage endpoint for the subnet that contain
 When using Azure Machine Learning curated environments, make sure to use the latest environment version. The container registry for the environment must also be `mcr.microsoft.com`. To check the container registry, use the following steps:
 
 1. From [Azure Machine Learning studio](https://ml.azure.com), select your workspace and then select __Environments__.
-1. Verify that the __Azure container registry__ begins with a value of `mcr.microsoft.com`.
+1. Verify that the __Azure Container Registry__ begins with a value of `mcr.microsoft.com`.
 
     > [!IMPORTANT]
     > If the container registry is `viennaglobal.azurecr.io` you cannot use the curated environment with the data exfiltration. Try upgrading to the latest version of the curated environment.
diff --git a/articles/machine-learning/includes/machine-learning-online-endpoint-troubleshooting.md b/articles/machine-learning/includes/machine-learning-online-endpoint-troubleshooting.md
@@ -43,7 +43,7 @@ If the value of `bypass` isn't `AzureServices`, use the guidance in [Configure A
 > This issue applies when you use the [legacy network isolation method for managed online endpoints](../concept-secure-online-endpoint.md#secure-outbound-access-with-legacy-network-isolation-method). In this method, Azure Machine Learning creates a managed virtual network for each deployment under an endpoint.
 
 1. Check whether the `egress-public-network-access` flag has a value of `disabled` for the deployment. If this flag is enabled, and the visibility of the container registry is private, this failure is expected.
-1. Use the following command to check the status of the private endpoint connection. Replace `<registry-name>` with the name of the Azure container registry for your workspace:
+1. Use the following command to check the status of the private endpoint connection. Replace `<registry-name>` with the name of the Azure Container Registry for your workspace:
 
    ```azurecli
    az acr private-endpoint-connection list -r <registry-name> --query "[?privateLinkServiceConnectionState.description=='Egress for Microsoft.MachineLearningServices/workspaces/onlineEndpoints'].{ID:id, status:privateLinkServiceConnectionState.status}"
diff --git a/articles/machine-learning/reference-yaml-workspace.md b/articles/machine-learning/reference-yaml-workspace.md
@@ -37,7 +37,7 @@ The source JSON schema can be found at https://azuremlschemas.azureedge.net/late
 | `resource_group` | string | **Required.** The resource group containing the workspace. If the resource group does not exist, a new one will be created. | | |
 | `hbi_workspace` | boolean | Whether the customer data is of high business impact (HBI), containing sensitive business information. For more information, see [Data encryption at rest](concept-data-encryption.md#encryption-at-rest). | | `false` |
 | `storage_account` | string | The fully qualified resource ID of an existing Azure storage account to use as the default storage account for the workspace. A storage account with premium storage or hierarchical namespace cannot be used as the default storage account. If omitted, a new storage account will be created. | | |
-| `container_registry` | string | The fully qualified resource ID of an existing Azure container registry to use as the default container registry for the workspace. Azure Machine Learning uses Azure Container Registry (ACR) for managing container images used for training and deployment. If omitted, a new container registry will be created. Creation is lazy loaded, so the container registry gets created the first time it is needed for an operation for either training or deployment. | | |
+| `container_registry` | string | The fully qualified resource ID of an existing Azure Container Registry to use as the default container registry for the workspace. Azure Machine Learning uses Azure Container Registry (ACR) for managing container images used for training and deployment. If omitted, a new container registry will be created. Creation is lazy loaded, so the container registry gets created the first time it is needed for an operation for either training or deployment. | | |
 | `key_vault` | string | The fully qualified resource ID of an existing Azure key vault to use as the default key vault for the workspace. If omitted, a new key vault will be created. | | |
 | `application_insights` | string | The fully qualified resource ID of an existing Azure application insights to use as the default application insights for the workspace. If omitted, a new application insights will be created. | | |
 | `customer_managed_key` | object | Azure Machine Learning stores metadata in an Azure Cosmos DB instance. By default the data is encrypted at rest with Microsoft-managed keys. To use your own customer-managed key for encryption, specify the customer-managed key information in this section. For more information, see [Data encryption for Azure Cosmos DB](concept-data-encryption.md#azure-cosmos-db). | | |
diff --git a/articles/machine-learning/v1/how-to-deploy-package-models.md b/articles/machine-learning/v1/how-to-deploy-package-models.md
@@ -44,7 +44,7 @@ Both packages can be used to get a local Docker image.
 
 ### Download a packaged model
 
-The following example builds an image, which is registered in the Azure container registry for your workspace:
+The following example builds an image, which is registered in the Azure Container Registry for your workspace:
 
 ```python
 package = Model.package(ws, [model], inference_config)
@@ -79,16 +79,16 @@ package = Model.package(ws, [model], inference_config, generate_dockerfile=True)
 package.wait_for_creation(show_output=True)
 # Download the package.
 package.save("./imagefiles")
-# Get the Azure container registry that the model/Dockerfile uses.
+# Get the Azure Container Registry that the model/Dockerfile uses.
 acr=package.get_container_registry()
 print("Address:", acr.address)
 print("Username:", acr.username)
 print("Password:", acr.password)
 ```
 
-This code downloads the files needed to build the image to the `imagefiles` directory. The Dockerfile included in the saved files references a base image stored in an Azure container registry. When you build the image on your local Docker installation, you need to use the address, user name, and password to authenticate to the registry. Use the following steps to build the image by using a local Docker installation:
+This code downloads the files needed to build the image to the `imagefiles` directory. The Dockerfile included in the saved files references a base image stored in an Azure Container Registry. When you build the image on your local Docker installation, you need to use the address, user name, and password to authenticate to the registry. Use the following steps to build the image by using a local Docker installation:
 
-1. From a shell or command-line session, use the following command to authenticate Docker with the Azure container registry. Replace `<address>`, `<username>`, and `<password>` with the values retrieved by `package.get_container_registry()`.
+1. From a shell or command-line session, use the following command to authenticate Docker with the Azure Container Registry. Replace `<address>`, `<username>`, and `<password>` with the values retrieved by `package.get_container_registry()`.
 
     ```bash
     docker login <address> -u <username> -p <password>
diff --git a/articles/machine-learning/v1/how-to-manage-workspace-cli.md b/articles/machine-learning/v1/how-to-manage-workspace-cli.md
@@ -101,7 +101,7 @@ When you deploy an Azure Machine Learning workspace, various other services are
 > * Hierarchical Namespace (ADLS Gen 2) is disabled
 > These requirements are only for the _default_ storage account used by the workspace.
 >
-> When attaching Azure container registry, you must have the [admin account](/azure/container-registry/container-registry-authentication#admin-account) enabled before it can be used with an Azure Machine Learning workspace.
+> When attaching Azure Container Registry, you must have the [admin account](/azure/container-registry/container-registry-authentication#admin-account) enabled before it can be used with an Azure Machine Learning workspace.
 
 # [Create with new resources](#tab/createnewresources)
 
diff --git a/articles/machine-learning/v1/how-to-train-with-custom-image.md b/articles/machine-learning/v1/how-to-train-with-custom-image.md
diff --git a/articles/search/vector-search-how-to-generate-embeddings.md b/articles/search/vector-search-how-to-generate-embeddings.md
