Merge pull request #2733 from MicrosoftDocs/main

v-alje · web-flow · commit d46a0e8799ee · 2025-02-04T15:05:16.000-08:00
02/04/2025 PM Publishing
diff --git a/articles/ai-services/computer-vision/faq.yml b/articles/ai-services/computer-vision/faq.yml
@@ -16,7 +16,7 @@ metadata:
 title: Azure AI Vision API Frequently Asked Questions
 summary: |
   > [!TIP]
-  > If you can't find answers to your questions in this FAQ, ask the Azure AI Vision API community on [StackOverflow](https://stackoverflow.com/questions/tagged/project-oxford+or+microsoft-cognitive) or contact Help and Support on [UserVoice](https://feedback.azure.com/d365community/forum/09041fae-0b25-ec11-b6e6-000d3a4f0858)
+  > If you can't find answers to your questions in this FAQ, ask the Azure AI Vision API community on [Stack Overflow](https://stackoverflow.com/questions/tagged/project-oxford+or+microsoft-cognitive) or contact Help and Support on [UserVoice](https://feedback.azure.com/d365community/forum/09041fae-0b25-ec11-b6e6-000d3a4f0858)
 
 
 sections:
diff --git a/articles/ai-services/content-safety/faq.yml b/articles/ai-services/content-safety/faq.yml
@@ -14,7 +14,7 @@ metadata:
 title: Azure AI Content Safety Frequently Asked Questions
 summary: |
   > [!TIP]
-  > If you can't find answers to your questions in this FAQ, ask the Cognitive Services API community on [StackOverflow](https://stackoverflow.com/questions/tagged/project-oxford+or+microsoft-cognitive) or contact Help and Support on [UserVoice](https://feedback.azure.com/d365community/forum/09041fae-0b25-ec11-b6e6-000d3a4f0858)
+  > If you can't find answers to your questions in this FAQ, ask the Cognitive Services API community on [Stack Overflow](https://stackoverflow.com/questions/tagged/project-oxford+or+microsoft-cognitive) or contact Help and Support on [UserVoice](https://feedback.azure.com/d365community/forum/09041fae-0b25-ec11-b6e6-000d3a4f0858)
 
 
 sections:
diff --git a/articles/ai-services/custom-vision-service/custom-vision-onnx-windows-ml.md b/articles/ai-services/custom-vision-service/custom-vision-onnx-windows-ml.md
@@ -58,7 +58,7 @@ To use your own image classifier model, follow these steps:
 To discover other ways to export and use a Custom Vision model, see the following documents:
 
 * [Export your model](./export-your-model.md)
-* [Use exported Tensorflow model in an Android application](https://github.com/Azure-Samples/cognitive-services-android-customvision-sample)
+* [Use exported TensorFlow model in an Android application](https://github.com/Azure-Samples/cognitive-services-android-customvision-sample)
 * [Use exported CoreML model in a Swift iOS application](https://go.microsoft.com/fwlink/?linkid=857726)
 * [Use exported CoreML model in an iOS application with Xamarin](https://github.com/xamarin/ios-samples/tree/master/ios11/CoreMLAzureModel)
 
diff --git a/articles/ai-services/custom-vision-service/export-model-python.md b/articles/ai-services/custom-vision-service/export-model-python.md
@@ -200,6 +200,6 @@ The results of running the image tensor through the model will then need to be m
 ## Related content
 
 Next, learn how to wrap your model into a mobile application:
-* [Use your exported Tensorflow model in an Android application](https://github.com/Azure-Samples/cognitive-services-android-customvision-sample)
+* [Use your exported TensorFlow model in an Android application](https://github.com/Azure-Samples/cognitive-services-android-customvision-sample)
 * [Use your exported CoreML model in a Swift iOS application](https://go.microsoft.com/fwlink/?linkid=857726)
 * [Use your exported CoreML model in an iOS application with Xamarin](https://github.com/xamarin/ios-samples/tree/master/ios11/CoreMLAzureModel)
diff --git a/articles/ai-services/custom-vision-service/export-programmatically.md b/articles/ai-services/custom-vision-service/export-programmatically.md
@@ -89,9 +89,9 @@ if export.status == "Done":
 
 Integrate your exported model into an application by exploring one of the following articles or samples:
 
-* [Use your Tensorflow model with Python](export-model-python.md)
+* [Use your TensorFlow model with Python](export-model-python.md)
 * [Use your ONNX model with Windows Machine Learning](custom-vision-onnx-windows-ml.md)
 * See the sample for [CoreML model in an iOS application](https://go.microsoft.com/fwlink/?linkid=857726) for real-time image classification with Swift.
-* See the sample for [Tensorflow model in an Android application](https://github.com/Azure-Samples/cognitive-services-android-customvision-sample) for real-time image classification on Android.
+* See the sample for [TensorFlow model in an Android application](https://github.com/Azure-Samples/cognitive-services-android-customvision-sample) for real-time image classification on Android.
 * See the sample for [CoreML model with Xamarin](https://github.com/xamarin/ios-samples/tree/master/ios11/CoreMLAzureModel) for real-time image classification in a Xamarin iOS app.
 * See the sample for how to use the exported model [(VAIDK/OpenVino)](https://github.com/Azure-Samples/customvision-export-samples)
diff --git a/articles/ai-services/custom-vision-service/faq.yml b/articles/ai-services/custom-vision-service/faq.yml
@@ -13,7 +13,7 @@ metadata:
 title: Custom Vision frequently asked questions
 summary: |
   > [!TIP]
-  > If you can't find answers to your questions in this FAQ, ask the Azure AI Vision API community on [StackOverflow](https://stackoverflow.com/questions/tagged/project-oxford+or+microsoft-cognitive) or contact Help and Support on [UserVoice](https://feedback.azure.com/d365community/forum/09041fae-0b25-ec11-b6e6-000d3a4f0858).
+  > If you can't find answers to your questions in this FAQ, ask the Azure AI Vision API community on [Stack Overflow](https://stackoverflow.com/questions/tagged/project-oxford+or+microsoft-cognitive) or contact Help and Support on [UserVoice](https://feedback.azure.com/d365community/forum/09041fae-0b25-ec11-b6e6-000d3a4f0858).
   
   
 sections:
diff --git a/articles/ai-services/openai/concepts/assistants.md b/articles/ai-services/openai/concepts/assistants.md
@@ -3,7 +3,7 @@ title: Azure OpenAI Service Assistants API concepts
 titleSuffix: Azure OpenAI Service
 description: Learn about the concepts behind the Azure OpenAI Assistants API.
 ms.topic: conceptual
-ms.date: 08/21/2024
+ms.date: 02/04/2025
 ms.service: azure-ai-openai
 manager: nitinme
 author: aahill
@@ -13,7 +13,7 @@ recommendations: false
 
 # Azure OpenAI Assistants API (Preview)
 
-Assistants, a new feature of Azure OpenAI Service, is now available in public preview. Assistants API makes it easier for developers to create applications with sophisticated copilot-like experiences that can sift through data, suggest solutions, and automate tasks.
+Assistants, a feature of Azure OpenAI Service, is available in public preview starting in the `2024-02-15-preview` API version. Assistants API makes it easier for developers to create applications with sophisticated copilot-like experiences that can sift through data, suggest solutions, and automate tasks.
 
 * Assistants can call Azure OpenAI’s [models](../concepts/models.md) with specific instructions to tune their personality and capabilities.
 * Assistants can access **multiple tools in parallel**. These can be both Azure OpenAI-hosted tools like [code interpreter](../how-to/code-interpreter.md) and [file search](../how-to/file-search.md), or tools you build, host, and access through [function calling](../how-to/function-calling.md).
@@ -38,6 +38,10 @@ Assistants API is built on the same capabilities that power OpenAI’s GPT produ
 > [!IMPORTANT]
 > Retrieving untrusted data using Function calling, Code Interpreter or File Search with file input, and Assistant Threads functionalities could compromise the security of your Assistant, or the application that uses the Assistant. Learn about mitigation approaches [here](https://aka.ms/oai/assistant-rai).
 
+## Available models
+
+To see a list of Azure OpenAI models that you can use with assitants, see the [Models](./models.md#assistants-preview) article.
+
 ## Assistants playground
 
 We provide a walkthrough of the Assistants playground in our [quickstart guide](../assistants-quickstart.md). This provides a no-code environment to test out the capabilities of assistants.
diff --git a/articles/ai-services/openai/media/use-your-data/search-trusted-service.png b/articles/ai-services/openai/media/use-your-data/search-trusted-service.png
diff --git a/articles/ai-services/translator/translator-faq.md b/articles/ai-services/translator/translator-faq.md
@@ -68,4 +68,6 @@ No. Customer data submitted for translation to Azure AI Translator isn't stored
 
 ---
 > [!TIP]
+
 > If you can't find answers to your questions in this FAQ, try asking the Azure AI Translator API community on [StackOverflow](https://stackoverflow.com/search?q=%5Bmicrosoft-cognitive%5D+or+%5Bmicrosoft-cognitive%5D+translator&s=34bf0ce2-b6b3-4355-86a6-d45a1121fe27).
+
diff --git a/articles/ai-studio/how-to/configure-managed-network.md b/articles/ai-studio/how-to/configure-managed-network.md
@@ -768,7 +768,7 @@ To allow installation of __Python packages for training and deployment__, add ou
 | `*.anaconda.org` | Used to get repo data. |
 | `pypi.org` | Used to list dependencies from the default index, if any, and the index isn't overwritten by user settings. If the index is overwritten, you must also allow `*.pythonhosted.org`. |
 | `pytorch.org`<br>`*.pytorch.org` | Used by some examples based on PyTorch. |
-| `*.tensorflow.org` | Used by some examples based on Tensorflow. |
+| `*.tensorflow.org` | Used by some examples based on TensorFlow. |
 
 ### Scenario: Use Visual Studio Code
 Visual Studio Code relies on specific hosts and ports to establish a remote connection.
diff --git a/articles/ai-studio/how-to/flow-deploy.md b/articles/ai-studio/how-to/flow-deploy.md
@@ -158,8 +158,6 @@ You can also specify the connections used by the endpoint when it performs infer
 
 Once you configured and reviewed all the steps above, you can select **Review + Create** to finish the creation.
 
-:::image type="content" source="../media/prompt-flow/how-to-deploy-for-real-time-inference/deploy-advanced-outputs-connections.png" alt-text="Screenshot of the advanced output and connections settings." lightbox = "../media/prompt-flow/how-to-deploy-for-real-time-inference/deploy-advanced-outputs-connections.png":::
-
 > [!NOTE]
 > Expect the endpoint creation to take approximately more than 15 minutes, as it contains several stages including creating endpoint, registering model, creating deployment, etc.
 >
@@ -174,7 +172,7 @@ If you enable this, tracing data and system metrics during inference time (such
 > [!IMPORTANT]
 > Granting permissions (adding role assignment) is only enabled to the **Owner** of the specific Azure resources. You might need to ask your Azure subscription owner (who might be your IT admin) for help.
 >
-> It's recommended to grant roles to the **user-assigned** identity **before the deployment creation**.
+> It's recommended to grant roles to the **user-assigned** identity as soon as the endpoint creation completes.
 > It might take more than 15 minutes for the granted permission to take effect.
 
 You can grant the required permissions in Azure portal UI by following steps.
@@ -200,7 +198,7 @@ You can grant the required permissions in Azure portal UI by following steps.
        
     :::image type="content" source="../media/prompt-flow/how-to-deploy-for-real-time-inference/storage-container-registry.png" alt-text="Screenshot of the overview page with storage and container registry highlighted." lightbox = "../media/prompt-flow/how-to-deploy-for-real-time-inference/storage-container-registry.png":::
 
-    Go to the hub container registry overview page, select **Access control**, and select **Add role assignment**, and assign **ACR pull |Pull container image** to the endpoint identity.
+    Go to the hub container registry overview page, select **Access control**, and select **Add role assignment**, and assign **ACR Pull** to the endpoint identity.
 
     Go to the hub default storage overview page, select **Access control**, and select **Add role assignment**, and assign **Storage Blob Data Reader** to the endpoint identity.
 
@@ -210,7 +208,7 @@ You can grant the required permissions in Azure portal UI by following steps.
 
 There will be notifications after you finish the deploy wizard. After the endpoint and deployment are created successfully, you can select **View details** in the notification to deployment detail page.
 
-You can also directly go to the **Deployments** page from the left navigation, select the deployment, and check the status.
+You can also directly go to the **Model + endpoints** page from the left navigation, select the deployment, and check the status.
 
 ## Test the endpoint
 
@@ -246,8 +244,8 @@ If you aren't going use the endpoint after completing this tutorial, you should
 > [!NOTE]
 > The complete deletion might take approximately 20 minutes.
 
-## Next Steps
+## Next steps
 
 - Learn more about what you can do in [Azure AI Foundry](../what-is-ai-studio.md)
 - Get answers to frequently asked questions in the [Azure AI FAQ article](../faq.yml)
-- [Enable trace and collect feedback for your deployment] (./develop/trace-production-sdk.md)
+- [Enable trace and collect feedback for your deployment](./develop/trace-production-sdk.md)
diff --git a/articles/ai-studio/media/prompt-flow/how-to-deploy-for-real-time-inference/deploy-advanced-deployment.png b/articles/ai-studio/media/prompt-flow/how-to-deploy-for-real-time-inference/deploy-advanced-deployment.png
diff --git a/articles/ai-studio/media/prompt-flow/how-to-deploy-for-real-time-inference/deploy-advanced-endpoint.png b/articles/ai-studio/media/prompt-flow/how-to-deploy-for-real-time-inference/deploy-advanced-endpoint.png
diff --git a/articles/ai-studio/media/prompt-flow/how-to-deploy-for-real-time-inference/deploy-advanced-outputs-connections.png b/articles/ai-studio/media/prompt-flow/how-to-deploy-for-real-time-inference/deploy-advanced-outputs-connections.png
diff --git a/articles/ai-studio/media/prompt-flow/how-to-deploy-for-real-time-inference/deploy-basic-settings.png b/articles/ai-studio/media/prompt-flow/how-to-deploy-for-real-time-inference/deploy-basic-settings.png
diff --git a/articles/machine-learning/azure-machine-learning-ci-image-release-notes.md b/articles/machine-learning/azure-machine-learning-ci-image-release-notes.md
@@ -88,7 +88,7 @@ python3-jupyter-core: `4.6.3-3`
 
 libzmq5: `4.3.2-2ubuntu1` 
 
-Tensorflow: `2.17.0`
+TensorFlow: `2.17.0`
 
 python3.8, python3.8-minimal: `3.8.10-0`
 
@@ -136,7 +136,7 @@ Ray: `2.31.0`
 
 Nvidia-docker2
 
-Tensorflow: `2.15.0`
+TensorFlow: `2.15.0`
 
 Pandas: `1.3.5`
 
@@ -242,7 +242,7 @@ Version: `24.01.30`
 
 Main changes:
 
-- Enable Tensorflow in GPU compute to detect GPU device.
+- Enable TensorFlow in GPU compute to detect GPU device.
 
 Main environment specific updates:
 
diff --git a/articles/machine-learning/concept-compute-target.md b/articles/machine-learning/concept-compute-target.md
@@ -203,7 +203,7 @@ If you use the GPU-enabled compute targets, it's important to ensure that the co
 In addition to ensuring the CUDA version and hardware are compatible, also ensure that the CUDA version is compatible with the version of the machine learning framework you're using:
 
 - For PyTorch, you can check the compatibility by visiting [PyTorch's previous versions page](https://pytorch.org/get-started/previous-versions/).
-- For Tensorflow, you can check the compatibility by visiting [Tensorflow's build from source page](https://www.tensorflow.org/install/source#gpu).
+- For TensorFlow, you can check the compatibility by visiting [TensorFlow's build from source page](https://www.tensorflow.org/install/source#gpu).
 
 ### Compute isolation
 
diff --git a/articles/machine-learning/data-science-virtual-machine/how-to-track-experiments.md b/articles/machine-learning/data-science-virtual-machine/how-to-track-experiments.md
@@ -152,7 +152,7 @@ Select __Create__.
 When we registered the model in our code using `register_model`, we specified the framework as **sklearn**. Azure Machine Learning supports no code deployments for these frameworks:
 
 * scikit-learn
-* Tensorflow SaveModel format
+* TensorFlow SaveModel format
 * ONNX model format
 
 No-code deployment means that you can deploy straight from the model artifact. You don't need to specify any specific scoring script.
diff --git a/articles/machine-learning/how-to-train-distributed-gpu.md b/articles/machine-learning/how-to-train-distributed-gpu.md
@@ -115,7 +115,7 @@ TF_CONFIG='{
 
 ### TensorFlow example
 
-* For the full notebook to run the TensorFlow example, see [azureml-examples: Train a basic neural network with distributed MPI on the MNIST dataset using Tensorflow with Horovod](https://github.com/Azure/azureml-examples/blob/main/sdk/python/jobs/single-step/tensorflow/mnist-distributed/tensorflow-mnist-distributed.ipynb).
+* For the full notebook to run the TensorFlow example, see [azureml-examples: Train a basic neural network with distributed MPI on the MNIST dataset using TensorFlow with Horovod](https://github.com/Azure/azureml-examples/blob/main/sdk/python/jobs/single-step/tensorflow/mnist-distributed/tensorflow-mnist-distributed.ipynb).
 
 ## Accelerating distributed GPU training with InfiniBand
 
diff --git a/articles/machine-learning/includes/machine-learning-public-internet-access.md b/articles/machine-learning/includes/machine-learning-public-internet-access.md
@@ -56,7 +56,7 @@ You may also need to allow __outbound__ traffic to Visual Studio Code and non-Mi
 | `pypi.org` | Used to list dependencies from the default index, if any, and the index isn't overwritten by user settings. If the index is overwritten, you must also allow `*.pythonhosted.org`. |
 | `cloud.r-project.org` | Used when installing CRAN packages for R development. |
 | `*.pytorch.org` | Used by some examples based on PyTorch. |
-| `*.tensorflow.org` | Used by some examples based on Tensorflow. |
+| `*.tensorflow.org` | Used by some examples based on TensorFlow. |
 | `code.visualstudio.com` | Required to download and install Visual Studio Code desktop. This isn't required for Visual Studio Code Web. |
 | `update.code.visualstudio.com`</br>`*.vo.msecnd.net` | Used to retrieve Visual Studio Code server bits that are installed on the compute instance through a setup script. |
 | `marketplace.visualstudio.com`</br>`vscode.blob.core.windows.net`</br>`*.gallerycdn.vsassets.io` | Required to download and install Visual Studio Code extensions. These hosts enable the remote connection to Compute Instances provided by the Azure ML extension for Visual Studio Code. For more information, see [Connect to an Azure Machine Learning compute instance in Visual Studio Code](../how-to-set-up-vs-code-remote.md). |
diff --git a/articles/machine-learning/includes/recommended-network-outbound.md b/articles/machine-learning/includes/recommended-network-outbound.md
@@ -15,4 +15,4 @@ ms.author: larryfr
 | `*.anaconda.org` | Used to get repo data. |
 | `pypi.org` | Used to list dependencies from the default index, if any, and the index isn't overwritten by user settings. If the index is overwritten, you must also allow `*.pythonhosted.org`. |
 | `pytorch.org`<br>`*.pytorch.org` | Used by some examples based on PyTorch. |
-| `*.tensorflow.org` | Used by some examples based on Tensorflow. |
+| `*.tensorflow.org` | Used by some examples based on TensorFlow. |
diff --git a/articles/machine-learning/v1/how-to-deploy-inferencing-gpus.md b/articles/machine-learning/v1/how-to-deploy-inferencing-gpus.md
@@ -1,7 +1,7 @@
 ---
 title: Deploy a model for inference with GPU
 titleSuffix: Azure Machine Learning
-description: This article teaches you how to use Azure Machine Learning to deploy a GPU-enabled Tensorflow deep learning model as a web service.service and score inference requests.
+description: This article teaches you how to use Azure Machine Learning to deploy a GPU-enabled TensorFlow deep learning model as a web service.service and score inference requests.
 services: machine-learning
 ms.service: azure-machine-learning
 ms.subservice: inferencing
@@ -44,7 +44,7 @@ Inference, or model scoring, is the phase where the deployed model is used to ma
 
     * To learn how to register models, see [Deploy Models](../how-to-deploy-online-endpoints.md).
 
-    * To create and register the Tensorflow model used to create this document, see [How to Train a TensorFlow Model](../how-to-train-tensorflow.md).
+    * To create and register the TensorFlow model used to create this document, see [How to Train a TensorFlow Model](../how-to-train-tensorflow.md).
 
 * A general understanding of [How and where to deploy models](../how-to-deploy-online-endpoints.md).
 
@@ -99,7 +99,7 @@ For more information on using AKS with Azure Machine Learning, see [How to deplo
 
 ## Write the entry script
 
-The entry script receives data submitted to the web service, passes it to the model, and returns the scoring results. The following script loads the Tensorflow model on startup, and then uses the model to score data.
+The entry script receives data submitted to the web service, passes it to the model, and returns the scoring results. The following script loads the TensorFlow model on startup, and then uses the model to score data.
 
 > [!TIP]
 > The entry script is specific to your model. For example, the script must know the framework to use with your model, data formats, etc.
@@ -140,7 +140,7 @@ This file is named `score.py`. For more information on entry scripts, see [How a
 
 ## Define the conda environment
 
-The conda environment file specifies the dependencies for the service. It includes dependencies required by both the model and the entry script. Please note that you must indicate azureml-defaults with verion >= 1.0.45 as a pip dependency, because it contains the functionality needed to host the model as a web service. The following YAML defines the environment for a Tensorflow model. It specifies `tensorflow-gpu`, which will make use of the GPU used in this deployment:
+The conda environment file specifies the dependencies for the service. It includes dependencies required by both the model and the entry script. Please note that you must indicate azureml-defaults with verion >= 1.0.45 as a pip dependency, because it contains the functionality needed to host the model as a web service. The following YAML defines the environment for a TensorFlow model. It specifies `tensorflow-gpu`, which will make use of the GPU used in this deployment:
 
 ```yaml
 name: project_environment
diff --git a/articles/machine-learning/v1/how-to-deploy-mlflow-models.md b/articles/machine-learning/v1/how-to-deploy-mlflow-models.md
diff --git a/articles/machine-learning/v1/how-to-monitor-datasets.md b/articles/machine-learning/v1/how-to-monitor-datasets.md
