Merge pull request #207992 from pritamso/Broken-link-fix-sdgilley

Broken link fixed

Author: denrea

articles/machine-learning/concept-compute-target.md

@@ -39,7 +39,7 @@ When performing inference, Azure Machine Learning creates a Docker container tha
 
 [!INCLUDE [aml-deploy-target](../../includes/aml-compute-target-deploy.md)]
 
-Learn [where and how to deploy your model to a compute target](how-to-deploy-and-where.md).
+Learn [where and how to deploy your model to a compute target](how-to-deploy-managed-online-endpoints.md).
 
 <a name="amlcompute"></a>
 ## Azure Machine Learning compute (managed)
@@ -172,4 +172,4 @@ For more information, see [set up compute targets for model training and deploym
 
 Learn how to:
 * [Use a compute target to train your model](how-to-set-up-training-targets.md)
-* [Deploy your model to a compute target](how-to-deploy-and-where.md)
+* [Deploy your model to a compute target](how-to-deploy-managed-online-endpoints.md)

articles/machine-learning/how-to-create-attach-compute-studio.md

@@ -34,7 +34,7 @@ In this article, learn how to create and manage compute targets in Azure Machine
 
 ## What's a compute target? 
 
-With Azure Machine Learning, you can train your model on a variety of resources or environments, collectively referred to as [__compute targets__](v1/concept-azure-machine-learning-architecture.md#compute-targets). A compute target can be a local machine or a cloud resource, such as an Azure Machine Learning Compute, Azure HDInsight, or a remote virtual machine.  You can also create compute targets for model deployment as described in ["Where and how to deploy your models"](how-to-deploy-and-where.md).
+With Azure Machine Learning, you can train your model on a variety of resources or environments, collectively referred to as [__compute targets__](v1/concept-azure-machine-learning-architecture.md#compute-targets). A compute target can be a local machine or a cloud resource, such as an Azure Machine Learning Compute, Azure HDInsight, or a remote virtual machine.  You can also create compute targets for model deployment as described in ["Where and how to deploy your models"](how-to-deploy-managed-online-endpoints.md).
 
 ## <a id="portal-view"></a>View compute targets
 
@@ -212,5 +212,5 @@ myvm = ComputeTarget(workspace=ws, name='my-vm-name')
 
 * Use the compute resource to [submit a training run](how-to-set-up-training-targets.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).
+* Once you have a trained model, learn [how and where to deploy models](how-to-deploy-managed-online-endpoints.md).
 * [Use Azure Machine Learning with Azure Virtual Networks](./how-to-network-security-overview.md)

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

@@ -233,6 +233,6 @@ method, or from the Experiment tab view in Azure Machine Learning studio client
 * [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).
+* Once you have a trained model, learn [how and where to deploy models](how-to-deploy-managed-online-endpoints.md).
 * View the [ScriptRunConfig class](/python/api/azureml-core/azureml.core.scriptrunconfig) SDK reference.
 * [Use Azure Machine Learning with Azure Virtual Networks](./how-to-network-security-overview.md)

articles/machine-learning/tutorial-train-deploy-notebook.md

@@ -363,7 +363,7 @@ Use these steps to delete your Azure Machine Learning workspace and all compute
 
 ## Next steps
 
-+ Learn about all of the [deployment options for Azure Machine Learning](how-to-deploy-and-where.md).
++ Learn about all of the [deployment options for Azure Machine Learning](how-to-deploy-managed-online-endpoints.md).
 + Learn how to [create clients for the web service](how-to-consume-web-service.md).
 + [Make predictions on large quantities of data](./tutorial-pipeline-batch-scoring-classification.md) asynchronously.
 + Monitor your Azure Machine Learning models with [Application Insights](./v1/how-to-enable-app-insights.md).

articles/machine-learning/v1/concept-azure-machine-learning-architecture.md

@@ -192,7 +192,7 @@ You deploy a [registered model](#model-registry) as a service endpoint. You need
 * **Scoring code**. This script accepts requests, scores the requests by using the model, and returns the results.
 * **Inference configuration**. The inference configuration specifies the environment, entry script, and other components needed to run the model as a service.
 
-For more information about these components, see [Deploy models with Azure Machine Learning](../how-to-deploy-and-where.md).
+For more information about these components, see [Deploy models with Azure Machine Learning](how-to-deploy-and-where.md).
 
 ### Endpoints
 
@@ -286,4 +286,4 @@ 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](../quickstart-create-resources.md)
-* [Tutorial: Train and deploy a model](../tutorial-train-deploy-notebook.md)
+* [Tutorial: Train and deploy a model](../tutorial-train-deploy-notebook.md)

articles/machine-learning/v1/how-to-attach-compute-targets.md

@@ -54,14 +54,14 @@ To use compute targets managed by Azure Machine Learning, see:
 
 ## What's a compute target?
 
-With Azure Machine Learning, you can train your model on various resources or environments, collectively referred to as [__compute targets__](concept-azure-machine-learning-architecture.md#compute-targets). A compute target can be a local machine or a cloud resource, such as an Azure Machine Learning Compute, Azure HDInsight, or a remote virtual machine.  You also use compute targets for model deployment as described in ["Where and how to deploy your models"](../how-to-deploy-and-where.md).
+With Azure Machine Learning, you can train your model on various resources or environments, collectively referred to as [__compute targets__](concept-azure-machine-learning-architecture.md#compute-targets). A compute target can be a local machine or a cloud resource, such as an Azure Machine Learning Compute, Azure HDInsight, or a remote virtual machine.  You also use compute targets for model deployment as described in ["Where and how to deploy your models"](how-to-deploy-and-where.md).
 
 
 ## Local computer
 
 When you use your local computer for **training**, there is no need to create a compute target.  Just [submit the training run](../how-to-set-up-training-targets.md) from your local machine.
 
-When you use your local computer for **inference**, you must have Docker installed. To perform the deployment, use [LocalWebservice.deploy_configuration()](/python/api/azureml-core/azureml.core.webservice.local.localwebservice#deploy-configuration-port-none-) to define the port that the web service will use. Then use the normal deployment process as described in [Deploy models with Azure Machine Learning](../how-to-deploy-and-where.md).
+When you use your local computer for **inference**, you must have Docker installed. To perform the deployment, use [LocalWebservice.deploy_configuration()](/python/api/azureml-core/azureml.core.webservice.local.localwebservice#deploy-configuration-port-none-) to define the port that the web service will use. Then use the normal deployment process as described in [Deploy models with Azure Machine Learning](how-to-deploy-and-where.md).
 
 ## Remote virtual machines
 
@@ -370,5 +370,5 @@ See these notebooks for examples of training with various compute targets:
 * Use the compute resource to [configure and submit a training run](../how-to-set-up-training-targets.md).
 * [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).
+* Once you have a trained model, learn [how and where to deploy models](../how-to-deploy-managed-online-endpoints.md).
 * [Use Azure Machine Learning with Azure Virtual Networks](../how-to-network-security-overview.md)