Merge pull request #3946 from cdpark/refresh-ai-batch3a

Feature 411216: Q&M: AI Freshness - Azure Machine Learning - batch 3

Author: ttorble

articles/machine-learning/media/tutorial-train-deploy-image-classification-model-vscode/create-workspace.png

@@ -891,7 +891,7 @@ items:
         href: ./v1/how-to-create-machine-learning-pipelines.md
       - name: Moving data into and between ML pipeline steps (Python)
         href: ./v1/how-to-move-data-in-out-of-pipelines.md
-      - name: Use automated ML in ML pipelines (Python)
+      - name: Use AutoML in machine learning pipelines (Python)
         displayName: machine learning automl
         href: ./v1/how-to-use-automlstep-in-pipelines.md
       - name: Deploy ML pipelines (Python)

articles/machine-learning/media/tutorial-train-deploy-image-classification-model-vscode/track-experiment-progress.png

@@ -1,27 +1,27 @@
 ---
-title: "Tutorial: Train image classification model: VS Code (preview)"
+title: "Train Image Classification Model with VS Code Extension"
 titleSuffix: Azure Machine Learning
-description: Learn how to train an image classification model using TensorFlow and the Azure Machine Learning Visual Studio Code Extension
+description: Learn how to train a TensorFlow image classification model using the Azure Machine Learning Visual Studio Code extension.
 services: machine-learning
 ms.service: azure-machine-learning
 ms.subservice: core
 ms.topic: tutorial
 author: ssalgadodev
 ms.author: ssalgado
 ms.reviewer: tbombach
-ms.date: 01/25/2024
+ms.date: 04/03/2025
 ms.custom:
   - cliv2
   - build-2023
   - ignite-2023
 #Customer intent: As a professional data scientist, I want to learn how to train an image classification model using TensorFlow and the Azure Machine Learning Visual Studio Code Extension.
 ---
 
-# Tutorial: Train an image classification TensorFlow model using the Azure Machine Learning Visual Studio Code Extension (preview)
+# Tutorial: Train an image classification model by using the Azure Machine Learning Visual Studio Code extension (preview)
 
 [!INCLUDE [cli v2](includes/machine-learning-cli-v2.md)]
 
-Learn how to train an image classification model to recognize hand-written numbers using TensorFlow and the Azure Machine Learning Visual Studio Code Extension.
+This article explains how to train an image classification model to recognize hand-written numbers by using TensorFlow and the Azure Machine Learning Visual Studio Code extension.
 
 [!INCLUDE [machine-learning-preview-generic-disclaimer](includes/machine-learning-preview-generic-disclaimer.md)]
 
@@ -34,33 +34,32 @@ In this tutorial, you learn the following tasks:
 
 ## Prerequisites
 
-- Azure subscription. If you don't have one, sign up to try the [free or paid version of Azure Machine Learning](https://azure.microsoft.com/free/). If you're using the free subscription, only CPU clusters are supported.
-- Install [Visual Studio Code](https://code.visualstudio.com/docs/setup/setup-overview), a lightweight, cross-platform code editor.
-- Azure Machine Learning studio Visual Studio Code extension. For install instructions, see the [Setup Azure Machine Learning Visual Studio Code extension guide](./how-to-setup-vs-code.md)
-- CLI (v2). For installation instructions, see [Install, set up, and use the CLI (v2)](how-to-configure-cli.md)
--  Clone the community driven repository
+- An Azure subscription. If you don't have one, sign up for a [free or paid version of Azure Machine Learning](https://azure.microsoft.com/free/). If you're using the free subscription, only CPU clusters are supported.
+- [Visual Studio Code](https://code.visualstudio.com/docs/setup/setup-overview), a lightweight, cross-platform code editor.
+- Azure Machine Learning studio Visual Studio Code extension. For installation instructions, see [Setup Azure Machine Learning Visual Studio Code extension](./how-to-setup-vs-code.md).
+- CLI (v2). For installation instructions, see [Install and set up the CLI (v2)](how-to-configure-cli.md)
+- Clone the community-driven [Azure Machine Learning examples repository](https://github.com/Azure/azureml-examples):
     ```bash
-        git clone https://github.com/Azure/azureml-examples.git
+    git clone https://github.com/Azure/azureml-examples.git
     ```
 
 ## Understand the code
 
-The code for this tutorial uses TensorFlow to train an image classification machine learning model that categorizes handwritten digits from 0-9. It does so by creating a neural network that takes the pixel values of 28 px x 28 px image as input and outputs a list of 10 probabilities, one for each of the digits being classified. This is a sample of what the data looks like.  
+The code for this tutorial uses TensorFlow to train an image classification machine learning model that categorizes handwritten digits from 0-9. It does so by creating a neural network that takes the pixel values of 28x28-pixel images as input, and then outputs a list of 10 probabilities, one for each of the digits being classified. The following sample shows what the data looks like.  
 
-![MNIST Digits](./media/tutorial-train-deploy-image-classification-model-vscode/digits.png)
+:::image type="content" source="media/tutorial-train-deploy-image-classification-model-vscode/digits.png" alt-text="Screenshot that shows a sample of MNIST digits.":::
 
 ## Create a workspace
 
-The first thing you have to do to build an application in Azure Machine Learning is to create a workspace. A workspace contains the resources to train models and the trained models themselves. For more information, see [what is a workspace](./concept-workspace.md).
+To build an application in Azure Machine Learning, you first need to create a workspace. A workspace contains the resources to train models and also the trained models themselves. For more information, see [What is an Azure Machine Learning workspace?](./concept-workspace.md)
 
-1. Open the *azureml-examples/cli/jobs/single-step/tensorflow/mnist* directory from the community driven repository in Visual Studio Code.
+1. In Visual Studio Code, open the *azureml-examples/cli/jobs/single-step/tensorflow/mnist* directory from the examples repository.
 1. On the Visual Studio Code activity bar, select the **Azure** icon to open the Azure Machine Learning view.
-1. In the Azure Machine Learning view, right-click your subscription node and select **Create Workspace**.
+1. Under **Machine Learning**, right-click your subscription node and select **Create workspace**.
 
-    > [!div class="mx-imgBorder"]
-    > ![Create workspace](./media/tutorial-train-deploy-image-classification-model-vscode/create-workspace.png)
+    :::image type="content" source="media/tutorial-train-deploy-image-classification-model-vscode/create-workspace.png" alt-text="Screenshot that shows the option to create a workspace.":::
 
-1. A specification file appears. Configure the specification file with the following options.
+1. A specification file appears. Configure the specification file with the following options and then save it.
 
     ```yml
     $schema: https://azuremlschemas.azureedge.net/latest/workspace.schema.json
@@ -78,14 +77,13 @@ The first thing you have to do to build an application in Azure Machine Learning
 1. Right-click the specification file and select **AzureML: Execute YAML**. Creating a resource uses the configuration options defined in the YAML specification file and submits a job using the CLI (v2). At this point, a request to Azure is made to create a new workspace and dependent resources in your account. After a few minutes, the new workspace appears in your subscription node.
 1. Set `TeamWorkspace` as your default workspace. Doing so places resources and jobs you create in the workspace by default. Select the **Set Azure Machine Learning Workspace** button on the Visual Studio Code status bar and follow the prompts to set `TeamWorkspace` as your default workspace.
 
-For more information on workspaces, see [how to manage resources in VS Code](how-to-manage-resources-vscode.md).
+For more information on workspaces, see [Manage Azure Machine Learning resources with the VS Code extension](how-to-manage-resources-vscode.md).
 
 ## Train the model
 
 During the training process, a TensorFlow model is trained by processing the training data and learning patterns embedded within it for each of the respective digits being classified.
 
-Like workspaces and compute targets, training jobs are defined using resource templates. For this sample, the specification is defined in the *job.yml* file, which looks like the following:
-
+Like workspaces and compute targets, training jobs are defined using resource templates. For this sample, the specification is defined in the *job.yml* file, which is located in the *azureml-examples/cli/jobs/single-step/tensorflow/mnist* folder in the examples repository.
 
 ```yml
 $schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json
@@ -100,7 +98,7 @@ experiment_name: tensorflow-mnist-example
 description: Train a basic neural network with TensorFlow on the MNIST dataset.
 ```
 
-This specification file submits a training job called `tensorflow-mnist-example` to the recently created `gpu-cluster` computer target that runs the code in the *train.py* Python script. The environment used is one of the curated environments provided by Azure Machine Learning which contains TensorFlow and other software dependencies required to run the training script. For more information on curated environments, see [Azure Machine Learning curated environments](resource-curated-environments.md).
+This specification file submits a training job called `tensorflow-mnist-example` to the recently created `gpu-cluster` computer target that runs the code in the *train.py* Python script. The environment used is one of the curated environments provided by Azure Machine Learning, which contains TensorFlow and other software dependencies required to run the training script. For more information on curated environments, see [Azure Machine Learning curated environments](resource-curated-environments.md).
 
 To submit the training job:
 
@@ -111,14 +109,13 @@ At this point, a request is sent to Azure to run your experiment on the selected
 
 When the dialog requesting to open an external website appears, select **Open**.
 
-> [!div class="mx-imgBorder"]
-> ![Track experiment progress](./media/tutorial-train-deploy-image-classification-model-vscode/track-experiment-progress.png)
+:::image type="content" source="media/tutorial-train-deploy-image-classification-model-vscode/track-experiment-progress.png" alt-text="Screenshot that shows the job's progress." lightbox="media/tutorial-train-deploy-image-classification-model-vscode/track-experiment-progress.png":::
 
-When the model is done training, the status label next to the run node updates to "Completed".
+When the model is done training, the status label next to the run node updates to *Completed*.
 
-## Next steps
+## Related content
 
-* [Launch Visual Studio Code integrated with Azure Machine Learning (preview)](how-to-launch-vs-code-remote.md)
-* For a walkthrough of how to edit, run, and debug code locally, see the [Python hello-world tutorial](https://code.visualstudio.com/docs/Python/Python-tutorial).
-* [Run Jupyter Notebooks in Visual Studio Code](how-to-manage-resources-vscode.md) using a remote Jupyter server.
-* For a walkthrough of how to train with Azure Machine Learning outside of Visual Studio Code, see [Tutorial: Train and deploy a model with Azure Machine Learning](tutorial-train-deploy-notebook.md).
+* [Launch Visual Studio Code integrated with Azure Machine Learning](how-to-launch-vs-code-remote.md)
+* [Get started with a Python tutorial](https://code.visualstudio.com/docs/Python/Python-tutorial)
+* [Manage Azure Machine Learning resources with the VS Code extension](how-to-manage-resources-vscode.md)
+* [Quickstart: Get started with Azure Machine Learning](tutorial-azure-ml-in-a-day.md)