Merge pull request #99117 from luisquintanilla/AB1656922

Update VS Code Extension Article

Author: GitHubber17

.openpublishing.redirection.json

@@ -14238,9 +14238,9 @@
       "redirect_document_id": true
     },
     {
-      "source_path": "articles/machine-learning/service/how-to-vscode-tools.md",
-      "redirect_url": "/azure/machine-learning/how-to-vscode-tools",
-      "redirect_document_id": true
+        "source_path": "articles/machine-learning/service/how-to-vscode-tools.md",
+        "redirect_url": "/azure/machine-learning/tutorial-setup-vscode-extension",
+        "redirect_document_id": true
     },
     {
       "source_path": "articles/machine-learning/service/monitor-azure-machine-learning.md",

articles/machine-learning/concept-azure-machine-learning-architecture.md

@@ -8,7 +8,7 @@ ms.subservice: core
 ms.topic: conceptual
 ms.author: larryfr
 author: Blackmist
-ms.date: 10/16/2019
+ms.date: 12/27/2019
 ms.custom: seoapril2019
 # As a data scientist, I want to understand the big picture about how Azure Machine Learning works.
 ms.custom: seodec18
@@ -43,8 +43,8 @@ Use these tools for Azure Machine Learning:
 
 +  Interact with the service in any Python environment with the [Azure Machine Learning SDK for Python](https://docs.microsoft.com/python/api/overview/azure/ml/intro?view=azure-ml-py).
 + Interact with the service in any R environment with the [Azure Machine Learning SDK for R](https://azure.github.io/azureml-sdk-for-r/reference/index.html).
-+ Automate your machine learning activities with the [Azure Machine Learning CLI](https://docs.microsoft.com/azure/machine-learning/reference-azure-machine-learning-cli).
-+ Write code in Visual Studio Code with [Azure Machine Learning VS Code extension](how-to-vscode-tools.md)
++ Automate your machine learning activities with the [Azure Machine Learning CLI](https://docs.microsoft.com/azure/machine-learning/service/reference-azure-machine-learning-cli).
++ Write code in Visual Studio Code with [Azure Machine Learning VS Code extension](tutorial-setup-vscode-extension.md)
 + Use [Azure Machine Learning designer (preview)](concept-designer.md) to perform the workflow steps without writing code.
 
 

articles/machine-learning/concept-workspace.md

@@ -8,7 +8,7 @@ ms.subservice: core
 ms.topic: conceptual
 ms.author: sgilley
 author: sdgilley
-ms.date: 11/04/2019
+ms.date: 12/27/2019
 # As a data scientist, I want to understand the purpose of a workspace for Azure Machine Learning.
 ---
 
@@ -88,7 +88,7 @@ There are multiple ways to create a workspace:
 * Use the [Azure portal](how-to-manage-workspace.md) for a point-and-click interface to walk you through each step.
 * Use the [Azure Machine Learning SDK for Python](https://docs.microsoft.com/python/api/overview/azure/ml/intro?view=azure-ml-py#workspace) to create a workspace on the fly from Python scripts or Jupiter notebooks
 * Use an [Azure Resource Manager template](how-to-create-workspace-template.md) or the [Azure Machine Learning CLI](reference-azure-machine-learning-cli.md) when you need to automate or customize the creation with corporate security standards.
-* If you work in Visual Studio Code, use the [VS Code extension](how-to-vscode-tools.md#get-started-with-azure-machine-learning-for-visual-studio-code).
+* If you work in Visual Studio Code, use the [VS Code extension](tutorial-setup-vscode-extension.md).
 
 ## <a name="upgrade"></a> Upgrade to Enterprise edition
 

articles/machine-learning/how-to-configure-environment.md

@@ -9,7 +9,7 @@ ms.service: machine-learning
 ms.subservice: core
 ms.reviewer: larryfr
 ms.topic: conceptual
-ms.date: 10/25/2019
+ms.date: 12/27/2019
 ms.custom: seodec18
 ---
 
@@ -254,7 +254,7 @@ To use Visual Studio Code for development:
 
 1. Install the Azure Machine Learning extension for Visual Studio Code, see [Azure Machine Learning](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.vscode-ai).
 
-    For more information, see [Use Azure Machine Learning for Visual Studio Code](how-to-vscode-tools.md).
+    For more information, see [Use Azure Machine Learning for Visual Studio Code](tutorial-setup-vscode-extension.md).
 
 1. Learn how to use Visual Studio Code for any type of Python development, see [Get started with Python in VSCode](https://code.visualstudio.com/docs/python/python-tutorial).
 

articles/machine-learning/how-to-deploy-and-where.md

@@ -9,7 +9,7 @@ ms.topic: conceptual
 ms.author: jordane
 author: jpe316
 ms.reviewer: larryfr
-ms.date: 12/17/2019
+ms.date: 12/27/2019
 
 ms.custom: seoapril2019
 ---
@@ -34,7 +34,7 @@ For more information on the concepts involved in the deployment workflow, see [M
 
 - A model. If you don't have a trained model, you can use the model and dependency files provided in [this tutorial](https://aka.ms/azml-deploy-cloud).
 
-- The [Azure CLI extension for the Machine Learning service](reference-azure-machine-learning-cli.md), the [Azure Machine Learning SDK for Python](https://docs.microsoft.com/python/api/overview/azure/ml/intro?view=azure-ml-py), or the [Azure Machine Learning Visual Studio Code extension](how-to-vscode-tools.md).
+- The [Azure CLI extension for the Machine Learning service](reference-azure-machine-learning-cli.md), the [Azure Machine Learning SDK for Python](https://docs.microsoft.com/python/api/overview/azure/ml/intro?view=azure-ml-py), or the [Azure Machine Learning Visual Studio Code extension](tutorial-setup-vscode-extension.md).
 
 ## Connect to your workspace
 
@@ -55,7 +55,7 @@ The following code shows how to connect to an Azure Machine Learning workspace b
 
 + **Using VS Code**
 
-   When you use VS Code, you select the workspace by using a graphical interface. For more information, see [Deploy and manage models](how-to-vscode-tools.md#deploy-and-manage-models) in the VS Code extension documentation.
+   When you use VS Code, you select the workspace by using a graphical interface. For more information, see [Deploy and manage models](tutorial-train-deploy-image-classification-model-vscode.md#deploy-the-model) in the VS Code extension documentation.
 
 ## <a id="registermodel"></a> Register your model
 
@@ -111,7 +111,7 @@ The code snippets in this section demonstrate how to register a model from a tra
 
 + **Using VS Code**
 
-  Register models using any model files or folders by using the [VS Code](how-to-vscode-tools.md#deploy-and-manage-models) extension.
+  Register models using any model files or folders by using the [VS Code](tutorial-train-deploy-image-classification-model-vscode.md#deploy-the-model) extension.
 
 ### Register a model from a local file
 

articles/machine-learning/how-to-deploy-azure-container-instance.md

@@ -9,7 +9,7 @@ ms.topic: conceptual
 ms.author: jordane
 author: jpe316
 ms.reviewer: larryfr
-ms.date: 10/25/2019
+ms.date: 12/27/2019
 ---
 
 # Deploy a model to Azure Container Instances
@@ -28,7 +28,7 @@ For information on quota and region availability for ACI, see [Quotas and region
 
 - A machine learning model registered in your workspace. If you don't have a registered model, see [How and where to deploy models](how-to-deploy-and-where.md).
 
-- The [Azure CLI extension for Machine Learning service](reference-azure-machine-learning-cli.md), [Azure Machine Learning Python SDK](https://docs.microsoft.com/python/api/overview/azure/ml/intro?view=azure-ml-py), or the [Azure Machine Learning Visual Studio Code extension](how-to-vscode-tools.md).
+- The [Azure CLI extension for Machine Learning service](reference-azure-machine-learning-cli.md), [Azure Machine Learning Python SDK](https://docs.microsoft.com/python/api/overview/azure/ml/intro?view=azure-ml-py), or the [Azure Machine Learning Visual Studio Code extension](tutorial-setup-vscode-extension.md).
 
 - The __Python__ code snippets in this article assume that the following variables are set:
 
@@ -76,7 +76,7 @@ For more information, see the [az ml model deploy](https://docs.microsoft.com/cl
 
 ## Using VS Code
 
-See [deploy your models with VS Code](how-to-vscode-tools.md#deploy-and-manage-models).
+See [deploy your models with VS Code](tutorial-train-deploy-image-classification-model-vscode.md#deploy-the-model).
 
 > [!IMPORTANT]
 > You don't need to create an ACI container to test in advance. ACI containers are created as needed.

articles/machine-learning/how-to-deploy-azure-kubernetes-service.md

@@ -9,7 +9,7 @@ ms.topic: conceptual
 ms.author: jordane
 author: jpe316
 ms.reviewer: larryfr
-ms.date: 11/06/2019
+ms.date: 01/16/2020
 ---
 
 # Deploy a model to an Azure Kubernetes Service cluster
@@ -38,7 +38,7 @@ When deploying to Azure Kubernetes Service, you deploy to an AKS cluster that is
 
 - A machine learning model registered in your workspace. If you don't have a registered model, see [How and where to deploy models](how-to-deploy-and-where.md).
 
-- The [Azure CLI extension for Machine Learning service](reference-azure-machine-learning-cli.md), [Azure Machine Learning Python SDK](https://docs.microsoft.com/python/api/overview/azure/ml/intro?view=azure-ml-py), or the [Azure Machine Learning Visual Studio Code extension](how-to-vscode-tools.md).
+- The [Azure CLI extension for Machine Learning service](reference-azure-machine-learning-cli.md), [Azure Machine Learning Python SDK](https://docs.microsoft.com/python/api/overview/azure/ml/intro?view=azure-ml-py), or the [Azure Machine Learning Visual Studio Code extension](tutorial-setup-vscode-extension.md).
 
 - The __Python__ code snippets in this article assume that the following variables are set:
 
@@ -104,7 +104,7 @@ For more information on the classes, methods, and parameters used in this exampl
 az ml computetarget create aks -n myaks
 ```
 
-For more information, see the [az ml computetarget create ask](https://docs.microsoft.com/cli/azure/ext/azure-cli-ml/ml/computetarget/create?view=azure-cli-latest#ext-azure-cli-ml-az-ml-computetarget-create-aks) reference.
+For more information, see the [az ml computetarget create aks](https://docs.microsoft.com/cli/azure/ext/azure-cli-ml/ml/computetarget/create?view=azure-cli-latest#ext-azure-cli-ml-az-ml-computetarget-create-aks) reference.
 
 ## Attach an existing AKS cluster
 
@@ -222,7 +222,7 @@ For more information, see the [az ml model deploy](https://docs.microsoft.com/cl
 
 ### Using VS Code
 
-For information on using VS Code, see [deploy to AKS via the VS Code extension](how-to-vscode-tools.md#deploy-and-manage-models).
+For information on using VS Code, see [deploy to AKS via the VS Code extension](tutorial-train-deploy-image-classification-model-vscode.md#deploy-the-model).
 
 > [!IMPORTANT]
 > Deploying through VS Code requires the AKS cluster to be created or attached to your workspace in advance.

articles/machine-learning/how-to-manage-workspace.md

@@ -9,15 +9,15 @@ ms.topic: conceptual
 
 ms.author: sgilley
 author: sdgilley
-ms.date: 11/04/2019
+ms.date: 12/27/2019
 ms.custom: seodec18
 
 ---
 
 # Create and manage Azure Machine Learning workspaces in the Azure portal
 [!INCLUDE [applies-to-skus](../../includes/aml-applies-to-basic-enterprise-sku.md)]
 
-In this article, you'll create, view, and delete [**Azure Machine Learning workspaces**](concept-workspace.md) in the Azure portal for [Azure Machine Learning](overview-what-is-azure-ml.md).  The portal is the easiest way to get started with workspaces but as your needs change or requirements for automation increase you can also create and delete workspaces [using the CLI](reference-azure-machine-learning-cli.md), [with Python code](https://docs.microsoft.com/python/api/overview/azure/ml/intro?view=azure-ml-py) or [via the VS Code extension](how-to-vscode-tools.md#get-started-with-azure-machine-learning-for-visual-studio-code).
+In this article, you'll create, view, and delete [**Azure Machine Learning workspaces**](concept-workspace.md) in the Azure portal for [Azure Machine Learning](overview-what-is-azure-ml.md).  The portal is the easiest way to get started with workspaces but as your needs change or requirements for automation increase you can also create and delete workspaces [using the CLI](reference-azure-machine-learning-cli.md), [with Python code](https://docs.microsoft.com/python/api/overview/azure/ml/intro?view=azure-ml-py) or [via the VS Code extension](tutorial-setup-vscode-extension.md).
 
 ## Create a workspace
 

articles/machine-learning/how-to-set-up-training-targets.md

@@ -9,7 +9,7 @@ ms.reviewer: sgilley
 ms.service: machine-learning
 ms.subservice: core
 ms.topic: conceptual
-ms.date: 11/21/2019
+ms.date: 01/16/2020
 ms.custom: seodec18
 ---
 # Set up and use compute targets for model training 
@@ -48,7 +48,7 @@ Learn more about [submitting experiments](#submit) at the end of this article.
 
 ## What's an estimator?
 
-To facilitate model training using popular frameworks, the Azure Machine Learning Python SDK provides an alternative higher-level abstraction, the estimator class.  This class allows you to easily construct run configurations. You can create and use a generic [Estimator](https://docs.microsoft.com/python/api/azureml-train-core/azureml.train.estimator?view=azure-ml-py) to submit training scripts that use any learning framework you choose (such as scikit-learn). We recommend using an estimator for training as it automatically contructs embedded objects like an environment or RunConfiguration objects for you. If you wish to have more control over how these objects are created and specfify what packages to install for your experiement run, follow [these steps](#amlcompute) to submit your training experiments using a RunConfiguration object on an Azure Machine Learning Compute.
+To facilitate model training using popular frameworks, the Azure Machine Learning Python SDK provides an alternative higher-level abstraction, the estimator class.  This class allows you to easily construct run configurations. You can create and use a generic [Estimator](https://docs.microsoft.com/python/api/azureml-train-core/azureml.train.estimator?view=azure-ml-py) to submit training scripts that use any learning framework you choose (such as scikit-learn). We recommend using an estimator for training as it automatically constructs embedded objects like an environment or RunConfiguration objects for you. If you wish to have more control over how these objects are created and specify what packages to install for your experiment run, follow [these steps](#amlcompute) to submit your training experiments using a RunConfiguration object on an Azure Machine Learning Compute.
 
 For PyTorch, TensorFlow, and Chainer tasks, Azure Machine Learning also provides respective [PyTorch](https://docs.microsoft.com/python/api/azureml-train-core/azureml.train.dnn.pytorch?view=azure-ml-py), [TensorFlow](https://docs.microsoft.com/python/api/azureml-train-core/azureml.train.dnn.tensorflow?view=azure-ml-py), and [Chainer](https://docs.microsoft.com/python/api/azureml-train-core/azureml.train.dnn.chainer?view=azure-ml-py) estimators to simplify using these frameworks.
 
@@ -359,7 +359,7 @@ For more information, see [Resource management](reference-azure-machine-learning
 
 ## Set up with VS Code
 
-You can access, create, and manage the compute targets that are associated with your workspace using the [VS Code extension](how-to-vscode-tools.md#create-and-manage-compute-targets) for Azure Machine Learning.
+You can access, create, and manage the compute targets that are associated with your workspace using the [VS Code extension](tutorial-train-deploy-image-classification-model-vscode.md#configure-compute-targets) for Azure Machine Learning.
 
 ## <a id="submit"></a>Submit training run using Azure Machine Learning SDK
 
@@ -409,7 +409,7 @@ Or you can:
 
 * Submit the experiment with an `Estimator` object as shown in [Train ML models with estimators](how-to-train-ml-models.md).
 * Submit a HyperDrive run for [hyperparameter tuning](how-to-tune-hyperparameters.md).
-* Submit an experiment via the [VS Code extension](how-to-vscode-tools.md#train-and-tune-models).
+* Submit an experiment via the [VS Code extension](tutorial-train-deploy-image-classification-model-vscode.md#train-the-model).
 
 For more information, see the [ScriptRunConfig](https://docs.microsoft.com/python/api/azureml-core/azureml.core.scriptrunconfig?view=azure-ml-py) and [RunConfiguration](https://docs.microsoft.com/python/api/azureml-core/azureml.core.runconfiguration?view=azure-ml-py) documentation.