Merge pull request #207 from changeworld/patch-1

v-dirichards · web-flow · commit ab419e828af4 · 2025-02-04T15:44:21.000-06:00
Fix typo
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-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/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
@@ -21,7 +21,7 @@ ms.custom: UpdateFrequency5, sdkv1
 
 MLflow is an open-source library for managing the life cycle of machine learning experiments. MLflow integration with Azure Machine Learning lets you extend the management capabilities beyond model training to the deployment phase of production models. In this article, you deploy an [MLflow](https://www.mlflow.org/) model as an Azure web service and apply Azure Machine Learning model management and data drift detection features to your production models.
 
-The following diagram demonstrates how the MLflow deploy API integrates with Azure Machine Learning to deploy models. You create models as Azure web services by using popular frameworks like PyTorch, Tensorflow, or scikit-learn, and manage the services in your workspace:
+The following diagram demonstrates how the MLflow deploy API integrates with Azure Machine Learning to deploy models. You create models as Azure web services by using popular frameworks like PyTorch, TensorFlow, or scikit-learn, and manage the services in your workspace:
 
 :::image type="content" source="./media/how-to-deploy-mlflow-models/mlflow-diagram-deploy.png" border="false" alt-text="Diagram that illustrates how the MLflow deploy API integrates with Azure Machine Learning to deploy models." lightbox="./media/how-to-deploy-mlflow-models/mlflow-diagram-deploy.png":::
 
