Merge pull request #184793 from sdgilley/sdg-update-tutorial

LJSpiller · web-flow · commit 5245b74a10ef · 2022-01-11T18:44:54.000-05:00
Update links to new tutorial
diff --git a/articles/machine-learning/concept-azure-machine-learning-architecture.md b/articles/machine-learning/concept-azure-machine-learning-architecture.md
@@ -160,7 +160,7 @@ You can bring a model that was trained outside of Azure Machine Learning. Or you
 
 Azure Machine Learning is framework agnostic. When you create a model, you can use any popular machine learning framework, such as Scikit-learn, XGBoost, PyTorch, TensorFlow, and Chainer.
 
-For an example of training a model using Scikit-learn, see [Tutorial: Train an image classification model with Azure Machine Learning](tutorial-train-models-with-aml.md).
+For an example of training a model using Scikit-learn, see [Tutorial: Train an image classification model with Azure Machine Learning](tutorial-train-deploy-notebook.md).
 
 
 ### <a name="register-model"></a> Model registry
@@ -178,7 +178,7 @@ When you register the model, you can provide additional metadata tags and then u
 
 You can't delete a registered model that is being used by an active deployment.
 
-For an example of registering a model, see [Train an image classification model with Azure Machine Learning](tutorial-train-models-with-aml.md).
+For an example of registering a model, see [Train an image classification model with Azure Machine Learning](tutorial-train-deploy-notebook.md).
 
 ## Deployment
 
@@ -216,7 +216,7 @@ Here are the details:
 [![Inference workflow](media/concept-azure-machine-learning-architecture/inferencing.png)](media/concept-azure-machine-learning-architecture/inferencing.png#lightbox)
 
 
-For an example of deploying a model as a web service, see [Deploy an image classification model in Azure Container Instances](tutorial-deploy-models-with-aml.md).
+For an example of deploying a model as a web service, see [Tutorial: Train and deploy a model](tutorial-train-deploy-notebook.md).
 
 #### Real-time endpoints
 
@@ -284,4 +284,4 @@ To get started with Azure Machine Learning, see:
 
 * [What is Azure Machine Learning?](overview-what-is-azure-machine-learning.md)
 * [Create an Azure Machine Learning workspace](how-to-manage-workspace.md)
-* [Tutorial (part 1): Train a model](tutorial-train-models-with-aml.md)
+* [Tutorial: Train and deploy a model](tutorial-train-deploy-notebook.md)
diff --git a/articles/machine-learning/concept-compute-instance.md b/articles/machine-learning/concept-compute-instance.md
@@ -110,7 +110,7 @@ You can also **[use a setup script (preview)](how-to-create-manage-compute-insta
 To create a compute instance for yourself, use your workspace in Azure Machine Learning studio, [create a new compute instance](how-to-create-manage-compute-instance.md?tabs=azure-studio#create) from either the **Compute** section or in the **Notebooks** section when you are ready to run one of your notebooks.
 
 You can also create an instance
-* Directly from the [integrated notebooks experience](tutorial-train-models-with-aml.md#azure)
+* Directly from the [integrated notebooks experience](tutorial-train-deploy-notebook.md#azure)
 * In Azure portal
 * From Azure Resource Manager template. For an example template, see the [create an Azure Machine Learning compute instance template](https://github.com/Azure/azure-quickstart-templates/tree/master/quickstarts/microsoft.machinelearningservices/machine-learning-compute-create-computeinstance).
 * With [Azure Machine Learning SDK](https://github.com/MicrosoftDocs/azure-docs/blob/master/articles/machine-learning/concept-compute-instance.md)
diff --git a/articles/machine-learning/concept-model-management-and-deployment.md b/articles/machine-learning/concept-model-management-and-deployment.md
@@ -204,7 +204,7 @@ Learn more by reading and exploring the following resources:
 
 + [How & where to deploy models](how-to-deploy-and-where.md) with Azure Machine Learning
 
-+ [Tutorial: Deploy an image classification model in ACI](tutorial-deploy-models-with-aml.md).
++ [Tutorial: Train and deploy a model](tutorial-train-deploy-notebook.md).
 
 + [End-to-end MLOps examples repo](https://github.com/microsoft/MLOps)
 
diff --git a/articles/machine-learning/how-to-assign-roles.md b/articles/machine-learning/how-to-assign-roles.md
@@ -488,5 +488,5 @@ Here are a few things to be aware of while you use Azure role-based access contr
 
 - [Enterprise security overview](concept-enterprise-security.md)
 - [Virtual network isolation and privacy overview](how-to-network-security-overview.md)
-- [Tutorial: Train models](tutorial-train-models-with-aml.md)
+- [Tutorial: Train and deploy a model](tutorial-train-deploy-notebook.md)
 - [Resource provider operations](../role-based-access-control/resource-provider-operations.md#microsoftmachinelearningservices)
diff --git a/articles/machine-learning/how-to-attach-compute-targets.md b/articles/machine-learning/how-to-attach-compute-targets.md
@@ -368,7 +368,7 @@ See these notebooks for examples of training with various compute targets:
 ## Next steps
 
 * Use the compute resource to [configure and submit a training run](how-to-set-up-training-targets.md).
-* [Tutorial: Train a model](tutorial-train-models-with-aml.md) uses a managed compute target to  train a model.
+* [Tutorial: Train and deploy a model](tutorial-train-deploy-notebook.md) uses a managed compute target to  train a model.
 * Learn how to [efficiently tune hyperparameters](how-to-tune-hyperparameters.md) to build better models.
 * Once you have a trained model, learn [how and where to deploy models](how-to-deploy-and-where.md).
 * [Use Azure Machine Learning with Azure Virtual Networks](./how-to-network-security-overview.md)
diff --git a/articles/machine-learning/how-to-configure-databricks-automl-environment.md b/articles/machine-learning/how-to-configure-databricks-automl-environment.md
@@ -166,5 +166,5 @@ Try it out:
 
 ## Next steps
 
-- [Train a model](tutorial-train-models-with-aml.md) on Azure Machine Learning with the MNIST dataset.
+- [Train and deploy a model](tutorial-train-deploy-notebook.md) on Azure Machine Learning with the MNIST dataset.
 - See the [Azure Machine Learning SDK for Python reference](/python/api/overview/azure/ml/intro).
diff --git a/articles/machine-learning/how-to-configure-environment.md b/articles/machine-learning/how-to-configure-environment.md
@@ -214,5 +214,5 @@ For more information, see [Data Science Virtual Machines](https://azure.microsof
 
 ## Next steps
 
-- [Train a model](tutorial-train-models-with-aml.md) on Azure Machine Learning with the MNIST dataset.
+- [Train and deploy a model](tutorial-train-deploy-notebook.md) on Azure Machine Learning with the MNIST dataset.
 - See the [Azure Machine Learning SDK for Python reference](/python/api/overview/azure/ml/intro). 
diff --git a/articles/machine-learning/how-to-create-attach-compute-studio.md b/articles/machine-learning/how-to-create-attach-compute-studio.md
@@ -212,7 +212,6 @@ myvm = ComputeTarget(workspace=ws, name='my-vm-name')
 ```
 
 * Use the compute resource to [submit a training run](how-to-set-up-training-targets.md).
-* [Tutorial: Train a model](tutorial-train-models-with-aml.md) uses a managed compute target to train a model.
 * Learn how to [efficiently tune hyperparameters](how-to-tune-hyperparameters.md) to build better models.
 * Once you have a trained model, learn [how and where to deploy models](how-to-deploy-and-where.md).
 * [Use Azure Machine Learning with Azure Virtual Networks](./how-to-network-security-overview.md)
diff --git a/articles/machine-learning/how-to-deploy-app-service.md b/articles/machine-learning/how-to-deploy-app-service.md
@@ -37,7 +37,7 @@ For more information on features provided by Azure App Service, see the [App Ser
 
 * An Azure Machine Learning workspace. For more information, see the [Create a workspace](how-to-manage-workspace.md) article.
 * The [Azure CLI](/cli/azure/install-azure-cli).
-* A trained machine learning model registered in your workspace. If you do not have a model, use the [Image classification tutorial: train model](tutorial-train-models-with-aml.md) to train and register one.
+* A trained machine learning model registered in your workspace. If you do not have a model, use the [Image classification tutorial: train model](tutorial-train-deploy-notebook.md) to train and register one.
 
     > [!IMPORTANT]
     > The code snippets in this article assume that you have set the following variables:
diff --git a/articles/machine-learning/how-to-deploy-functions.md b/articles/machine-learning/how-to-deploy-functions.md
@@ -28,7 +28,7 @@ With Azure Machine Learning, you can create Docker images from trained machine l
 
 * An Azure Machine Learning workspace. For more information, see the [Create a workspace](how-to-manage-workspace.md) article.
 * The [Azure CLI](/cli/azure/install-azure-cli).
-* A trained machine learning model registered in your workspace. If you do not have a model, use the [Image classification tutorial: train model](tutorial-train-models-with-aml.md) to train and register one.
+* A trained machine learning model registered in your workspace. If you do not have a model, use the [Image classification tutorial: train model](tutorial-train-deploy-notebook.md) to train and register one.
 
     > [!IMPORTANT]
     > The code snippets in this article assume that you have set the following variables:
diff --git a/articles/machine-learning/how-to-deploy-local.md b/articles/machine-learning/how-to-deploy-local.md
@@ -25,7 +25,7 @@ Scenarios for local deployment include:
 ## Prerequisites
 
 - An Azure Machine Learning workspace. For more information, see [Create an Azure Machine Learning workspace](how-to-manage-workspace.md).
-- A model and an environment. If you don't have a trained model, you can use the model and dependency files provided in [this tutorial](tutorial-train-models-with-aml.md).
+- A model and an environment. If you don't have a trained model, you can use the model and dependency files provided in [this tutorial](tutorial-train-deploy-notebook.md).
 - The [Azure Machine Learning SDK for Python](/python/api/overview/azure/ml/intro).
 - A conda manager, like Anaconda or Miniconda, if you want to mirror Azure Machine Learning package dependencies.
 - Docker, if you want to use a containerized version of the Azure Machine Learning environment.
@@ -56,7 +56,7 @@ The argument to the `run()` method will be in this form:
 
 The object you return from the `run()` method must implement `toJSON() -> string`.
 
-The following example demonstrates how to load a registered scikit-learn model and score it by using NumPy data. This example is based on the model and dependencies of [this tutorial](tutorial-train-models-with-aml.md).
+The following example demonstrates how to load a registered scikit-learn model and score it by using NumPy data. This example is based on the model and dependencies of [this tutorial](tutorial-train-deploy-notebook.md).
 
 ```python
 import json
diff --git a/articles/machine-learning/how-to-enable-app-insights.md b/articles/machine-learning/how-to-enable-app-insights.md
@@ -35,7 +35,7 @@ The [enable-app-insights-in-production-service.ipynb](https://github.com/Azure/M
 
 * An Azure Machine Learning workspace, a local directory that contains your scripts, and the Azure Machine Learning SDK for Python installed. To learn more, see [How to configure a development environment](how-to-configure-environment.md).
 
-* A trained machine learning model. To learn more, see the [Train image classification model](tutorial-train-models-with-aml.md) tutorial.
+* A trained machine learning model. To learn more, see the [Train image classification model](tutorial-train-deploy-notebook.md) tutorial.
 
 <a name="python"></a>
 
diff --git a/articles/machine-learning/how-to-enable-data-collection.md b/articles/machine-learning/how-to-enable-data-collection.md
@@ -57,7 +57,7 @@ The path to the output data in the blob follows this syntax:
 
 - An Azure Machine Learning workspace, a local directory containing your scripts, and the Azure Machine Learning SDK for Python must be installed. To learn how to install them, see [How to configure a development environment](how-to-configure-environment.md).
 
-- You need a trained machine-learning model to be deployed to AKS. If you don't have a model, see the [Train image classification model](tutorial-train-models-with-aml.md) tutorial.
+- You need a trained machine-learning model to be deployed to AKS. If you don't have a model, see the [Train image classification model](tutorial-train-deploy-notebook.md) tutorial.
 
 - You need an AKS cluster. For information on how to create one and deploy to it, see [How to deploy and where](how-to-deploy-and-where.md).
 
diff --git a/articles/machine-learning/how-to-homomorphic-encryption-seal.md b/articles/machine-learning/how-to-homomorphic-encryption-seal.md
@@ -32,7 +32,7 @@ The encryption method used in this sample is [homomorphic encryption](https://gi
 
 ## Prerequisites
 
-This guide assumes that you have an image classification model registered in Azure Machine Learning. If not, register the model using a [pretrained model](https://github.com/Azure/MachineLearningNotebooks/raw/master/tutorials/image-classification-mnist-data/sklearn_mnist_model.pkl) or create your own by completing the [train an image classification model with Azure Machine Learning tutorial](tutorial-train-models-with-aml.md).
+This guide assumes that you have an image classification model registered in Azure Machine Learning. If not, register the model using a [pretrained model](https://github.com/Azure/MachineLearningNotebooks/raw/master/tutorials/image-classification-mnist-data/sklearn_mnist_model.pkl) or create your own by completing the [train an image classification model with Azure Machine Learning tutorial](tutorial-train-deploy-notebook.md).
 
 ## Configure local environment
 
diff --git a/articles/machine-learning/how-to-label-data.md b/articles/machine-learning/how-to-label-data.md
@@ -198,4 +198,4 @@ When you're done labeling, select your name in the upper-right corner of the lab
 
 ## Next steps
 
-* Learn to [train image classification models in Azure](./tutorial-train-models-with-aml.md)
+* Learn to [train image classification models in Azure](./tutorial-train-deploy-notebook.md)
diff --git a/articles/machine-learning/how-to-log-view-metrics.md b/articles/machine-learning/how-to-log-view-metrics.md
@@ -251,4 +251,4 @@ The following notebooks demonstrate concepts in this article:
 
 See these articles to learn more on how to use Azure Machine Learning:
 
-* See an example of how to register the best model and deploy it in the tutorial, [Train an image classification model with Azure Machine Learning](tutorial-train-models-with-aml.md).
+* See an example of how to register the best model and deploy it in the tutorial, [Train an image classification model with Azure Machine Learning](tutorial-train-deploy-notebook.md).
diff --git a/articles/machine-learning/how-to-manage-files.md b/articles/machine-learning/how-to-manage-files.md
@@ -54,7 +54,7 @@ To create a new file in a different folder:
 
 Your workspace contains a **Sample notebooks** folder with notebooks designed to help you explore the SDK and serve as examples for your own machine learning projects.   Clone these notebooks into your own folder to run and edit them.  
 
-For an example, see [Tutorial: Create your first ML experiment](tutorial-train-models-with-aml.md#azure).
+For an example, see [Tutorial: Create your first ML experiment](tutorial-train-deploy-notebook.md#azure).
 
 ## Share files
 
diff --git a/articles/machine-learning/how-to-manage-workspace.md b/articles/machine-learning/how-to-manage-workspace.md
@@ -400,7 +400,7 @@ Examples of creating a workspace:
 
 ## Next steps
 
-Once you have a workspace, learn how to [Train and deploy a model](tutorial-train-models-with-aml.md).
+Once you have a workspace, learn how to [Train and deploy a model](tutorial-train-deploy-notebook.md).
 
 To learn more about planning a workspace for your organization's requirements, see [Organize and set up Azure Machine Learning](/azure/cloud-adoption-framework/ready/azure-best-practices/ai-machine-learning-resource-organization).
 
diff --git a/articles/machine-learning/how-to-set-up-training-targets.md b/articles/machine-learning/how-to-set-up-training-targets.md
@@ -228,7 +228,7 @@ method, or from the Experiment tab view in Azure Machine Learning studio client
 
 ## Next steps
 
-* [Tutorial: Train a model](tutorial-train-models-with-aml.md) uses a managed compute target to  train a model.
+* [Tutorial: Train and deploy a model](tutorial-train-deploy-notebook.md) uses a managed compute target to  train a model.
 * See how to train models with specific ML frameworks, such as [Scikit-learn](how-to-train-scikit-learn.md), [TensorFlow](how-to-train-tensorflow.md), and [PyTorch](how-to-train-pytorch.md).
 * Learn how to [efficiently tune hyperparameters](how-to-tune-hyperparameters.md) to build better models.
 * Once you have a trained model, learn [how and where to deploy models](how-to-deploy-and-where.md).
diff --git a/articles/machine-learning/how-to-troubleshoot-deployment-local.md b/articles/machine-learning/how-to-troubleshoot-deployment-local.md
@@ -149,5 +149,5 @@ Learn more about deployment:
 * [How to troubleshoot remote deployments](how-to-troubleshoot-deployment.md)
 * [Azure Machine Learning inference HTTP Server](how-to-inference-server-http.md)
 * [How to deploy and where](how-to-deploy-and-where.md)
-* [Tutorial: Train & deploy models](tutorial-train-models-with-aml.md)
+* [Tutorial: Train & deploy models](tutorial-train-deploy-notebook.md)
 * [How to run and debug experiments locally](./how-to-debug-visual-studio-code.md)
diff --git a/articles/machine-learning/how-to-troubleshoot-deployment.md b/articles/machine-learning/how-to-troubleshoot-deployment.md
@@ -226,5 +226,5 @@ For more information, visit the [interactive debugging in VS Code guide](how-to-
 Learn more about deployment:
 
 * [How to deploy and where](how-to-deploy-and-where.md)
-* [Tutorial: Train & deploy models](tutorial-train-models-with-aml.md)
+* [Tutorial: Train & deploy models](tutorial-train-deploy-notebook.md)
 * [How to run and debug experiments locally](./how-to-debug-visual-studio-code.md)
diff --git a/articles/machine-learning/how-to-use-environments.md b/articles/machine-learning/how-to-use-environments.md
@@ -442,6 +442,5 @@ Using the Azure Machine Learning extension, you can create and manage environmen
 
 ## Next steps
 
-* To use a managed compute target to train a model, see [Tutorial: Train a model](tutorial-train-models-with-aml.md).
 * After you have a trained model, learn [how and where to deploy models](how-to-deploy-and-where.md).
 * View the [`Environment` class SDK reference](/python/api/azureml-core/azureml.core.environment%28class%29).
diff --git a/articles/machine-learning/how-to-use-managed-identities.md b/articles/machine-learning/how-to-use-managed-identities.md
@@ -71,7 +71,7 @@ If you do not bring your own ACR, Azure Machine Learning service will create one
     az ml workspace show -n <my workspace> -g <my resource group>
     ```
 
-1. Perform an action that requires ACR. For example, the [tutorial on training a model](tutorial-train-models-with-aml.md).
+1. Perform an action that requires ACR. For example, the [tutorial on training a model](tutorial-train-deploy-notebook.md).
 
 1. Get the ACR name created by the cluster:
 
diff --git a/articles/machine-learning/how-to-use-reinforcement-learning.md b/articles/machine-learning/how-to-use-reinforcement-learning.md
@@ -38,7 +38,7 @@ Run this code in either of these environments. We recommend you try Azure Machin
 
  - Azure Machine Learning compute instance
 
-     - Learn how to clone sample notebooks in [Tutorial: Setup environment and workspace](tutorial-train-models-with-aml.md).
+     - Learn how to clone sample notebooks in [Tutorial: Train and deploy a model](tutorial-train-deploy-notebook.md).
          - Clone the **how-to-use-azureml** folder instead of **tutorials**
      - Run the virtual network setup notebook located at `/how-to-use-azureml/reinforcement-learning/setup/devenv_setup.ipynb` to open network ports used for distributed reinforcement learning.
      - Run the sample notebook `/how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/pong_rllib.ipynb`
diff --git a/articles/machine-learning/overview-what-happened-to-workbench.md b/articles/machine-learning/overview-what-happened-to-workbench.md
@@ -55,7 +55,7 @@ The portal's workspace dashboard is supported on Microsoft Edge, Chrome, and Fir
 
 [![Online portal](./media/overview-what-happened-to-workbench/image001.png)](./media/overview-what-happened-to-workbench/image001.png#lightbox)
 
-Start training your models and tracking the run histories using the new CLI and SDK. You can learn how with the [Tutorial: train models with Azure Machine Learning](tutorial-train-models-with-aml.md).
+Start training your models and tracking the run histories using the new CLI and SDK. You can learn how with the [Tutorial: train models with Azure Machine Learning](tutorial-train-deploy-notebook.md).
 
 ## Will projects persist?
 
@@ -83,7 +83,7 @@ In the latest version, models are deployed as web services to Azure Container In
 
 Learn more in these articles:
 + [Where and how to deploy models](how-to-deploy-and-where.md)
-+ [Tutorial: Deploy models with Azure Machine Learning](tutorial-deploy-models-with-aml.md)
++ [Tutorial: Train and deploy a model](tutorial-train-deploy-notebook.md)
 
 ## Next steps
 
@@ -94,7 +94,7 @@ For an overview of the service, read [What is Azure Machine Learning?](overview-
 Start with [Quickstart: Get started with Azure Machine Learning](quickstart-create-resources.md).  Then use these resources to create your first experiment with your preferred method:
 
   + [Run a "Hello world!" Python script (part 1 of 3)](tutorial-1st-experiment-hello-world.md)
-  + [Use a Jupyter notebook to train image classification models](tutorial-train-models-with-aml.md)
+  + [Use a Jupyter notebook to train image classification models](tutorial-train-deploy-notebook.md)
   + [Use automated machine learning](tutorial-designer-automobile-price-train-score.md) 
   + [Use the designer's drag & drop capabilities](tutorial-first-experiment-automated-ml.md) 
   + [Use the ML extension to the CLI](how-to-train-cli.md)
diff --git a/articles/machine-learning/overview-what-is-machine-learning-studio.md b/articles/machine-learning/overview-what-is-machine-learning-studio.md
@@ -101,7 +101,7 @@ Visit the [studio](https://ml.azure.com), or explore the different authoring opt
 Start with [Quickstart: Get started with Azure Machine Learning](quickstart-create-resources.md).  Then use these resources to create your first experiment with your preferred method:
 
   + [Run a "Hello world!" Python script (part 1 of 3)](tutorial-1st-experiment-hello-world.md)
-  + [Use a Jupyter notebook to train image classification models](tutorial-train-models-with-aml.md)
+  + [Use a Jupyter notebook to train image classification models](tutorial-train-deploy-notebook.md)
   + [Use automated machine learning to train & deploy models](tutorial-first-experiment-automated-ml.md)  
   + [Use the designer to train & deploy models](tutorial-designer-automobile-price-train-score.md)
   + [Use studio in a secured virtual network](how-to-enable-studio-virtual-network.md)
diff --git a/articles/machine-learning/samples-notebooks.md b/articles/machine-learning/samples-notebooks.md
diff --git a/articles/machine-learning/tutorial-auto-train-models.md b/articles/machine-learning/tutorial-auto-train-models.md
diff --git a/articles/machine-learning/tutorial-train-deploy-image-classification-model-vscode.md b/articles/machine-learning/tutorial-train-deploy-image-classification-model-vscode.md
