Merge pull request #214928 from cmcclister/cm-linkfix-20221018-5

LinkFix: azure-docs-pr (2022-10) - 5

Author: JamesJBarnett

articles/machine-learning/how-to-prevent-data-loss-exfiltration.md

@@ -14,7 +14,7 @@ ms.date: 08/26/2022
 
 # Azure Machine Learning data exfiltration prevention (Preview)
 
-<!-- Learn how to use a [Service Endpoint policy](/azure/virtual-network/virtual-network-service-endpoint-policies-overview) to prevent data exfiltration from storage accounts in your Azure Virtual Network that are used by Azure Machine Learning. -->
+<!-- Learn how to use a [Service Endpoint policy](../virtual-network/virtual-network-service-endpoint-policies-overview.md) to prevent data exfiltration from storage accounts in your Azure Virtual Network that are used by Azure Machine Learning. -->
 
 Azure Machine Learning has several inbound and outbound dependencies. Some of these dependencies can expose a data exfiltration risk by malicious agents within your organization. This document explains how to minimize data exfiltration risk by limiting inbound and outbound requirements.
 
@@ -151,4 +151,4 @@ When using Azure ML curated environments, make sure to use the latest environmen
 For more information, see the following articles:
 
 * [How to configure inbound and outbound network traffic](how-to-access-azureml-behind-firewall.md)
-* [Azure Batch simplified node communication](/azure/batch/simplified-compute-node-communication)
+* [Azure Batch simplified node communication](../batch/simplified-compute-node-communication.md)

articles/machine-learning/how-to-secure-inferencing-vnet.md

@@ -47,7 +47,7 @@ In this article, you learn how to secure inferencing environments (online endpoi
     - "Microsoft.Network/virtualNetworks/join/action" on the virtual network resource.
     - "Microsoft.Network/virtualNetworks/subnet/join/action" on the subnet resource.
 
-    For more information on Azure RBAC with networking, see the [Networking built-in roles](/azure/role-based-access-control/built-in-roles#networking).
+    For more information on Azure RBAC with networking, see the [Networking built-in roles](../role-based-access-control/built-in-roles.md#networking).
 
 + If using Azure Kubernetes Service (AKS), you must have an existing AKS cluster secured as described in the [Secure Azure Kubernetes Service inference environment](how-to-secure-kubernetes-inferencing-environment.md) article.
 
@@ -77,4 +77,4 @@ This article is part of a series on securing an Azure Machine Learning workflow.
 * [Secure the training environment](how-to-secure-training-vnet.md)
 * [Enable studio functionality](how-to-enable-studio-virtual-network.md)
 * [Use custom DNS](how-to-custom-dns.md)
-* [Use a firewall](how-to-access-azureml-behind-firewall.md)
+* [Use a firewall](how-to-access-azureml-behind-firewall.md)

articles/machine-learning/how-to-secure-kubernetes-inferencing-environment.md

@@ -23,7 +23,7 @@ If you have an Azure Kubernetes (AKS) cluster behind of VNet, you would need to
 
 * If your AKS cluster is behind of a VNet, your workspace and its associated resources (storage, key vault, Azure Container Registry) must have private endpoints or service endpoints in the same or peered VNet as AKS cluster's VNet. For more information on securing the workspace and associated resources, see [create a secure workspace](tutorial-create-secure-workspace.md).
 * If your workspace has a __private endpoint__, the Azure Kubernetes Service cluster must be in the same Azure region as the workspace.
-* Using a [public fully qualified domain name (FQDN) with a private AKS cluster](/azure/aks/private-clusters) is __not supported__ with Azure Machine learning.
+* Using a [public fully qualified domain name (FQDN) with a private AKS cluster](../aks/private-clusters.md) is __not supported__ with Azure Machine learning.
 
 ## What is a secure AKS inferencing environment
 

articles/machine-learning/how-to-secure-training-vnet.md

@@ -93,7 +93,7 @@ In this article you learn how to secure the following training compute resources
         > 
         > If you have another NSG at the subnet level, the rules in the subnet level NSG mustn't conflict with the rules in the automatically created NSG.
         >
-        > To learn how the NSGs filter your network traffic, see [How network security groups filter network traffic](/azure/virtual-network/network-security-group-how-it-works).
+        > To learn how the NSGs filter your network traffic, see [How network security groups filter network traffic](../virtual-network/network-security-group-how-it-works.md).
 
         * Allow inbound TCP traffic on ports 29876-29877 from the `BatchNodeManagement` service tag.
         * Allow inbound TCP traffic on port 44224 from the `AzureMachineLearning` service tag.

articles/machine-learning/how-to-train-distributed-gpu.md

@@ -416,6 +416,6 @@ If you create an `AmlCompute` cluster of one of these RDMA-capable, InfiniBand-e
 
 ## Next steps
 
-* [Deploy machine learning models to Azure](/azure/machine-learning/how-to-deploy-managed-online-endpoints)
+* [Deploy machine learning models to Azure](./how-to-deploy-managed-online-endpoints.md)
 * [Deploy and score a machine learning model by using a managed online endpoint (preview)](how-to-deploy-managed-online-endpoints.md)
-* [Reference architecture for distributed deep learning training in Azure](/azure/architecture/reference-architectures/ai/training-deep-learning)
+* [Reference architecture for distributed deep learning training in Azure](/azure/architecture/reference-architectures/ai/training-deep-learning)

articles/machine-learning/how-to-train-keras.md

@@ -91,7 +91,7 @@ The result of running this script is a workspace handle that you'll use to manag
 
 AzureML needs a compute resource to run a job. This resource can be single or multi-node machines with Linux or Windows OS, or a specific compute fabric like Spark.
 
-In the following example script, we provision a Linux [`compute cluster`](/azure/machine-learning/how-to-create-attach-compute-cluster?tabs=python). You can see the [`Azure Machine Learning pricing`](https://azure.microsoft.com/pricing/details/machine-learning/) page for the full list of VM sizes and prices. Since we need a GPU cluster for this example, let's pick a *STANDARD_NC6* model and create an AzureML compute.
+In the following example script, we provision a Linux [`compute cluster`](./how-to-create-attach-compute-cluster.md?tabs=python). You can see the [`Azure Machine Learning pricing`](https://azure.microsoft.com/pricing/details/machine-learning/) page for the full list of VM sizes and prices. Since we need a GPU cluster for this example, let's pick a *STANDARD_NC6* model and create an AzureML compute.
 
 [!notebook-python[](~/azureml-examples-main/sdk/python/jobs/single-step/tensorflow/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.ipynb?name=cpu_compute_target)]
 

articles/machine-learning/how-to-train-pytorch.md

@@ -86,7 +86,7 @@ The result of running this script is a workspace handle that you'll use to manag
 
 AzureML needs a compute resource to run a job. This resource can be single or multi-node machines with Linux or Windows OS, or a specific compute fabric like Spark.
 
-In the following example script, we provision a Linux [`compute cluster`](/azure/machine-learning/how-to-create-attach-compute-cluster?tabs=python). You can see the [`Azure Machine Learning pricing`](https://azure.microsoft.com/pricing/details/machine-learning/) page for the full list of VM sizes and prices. Since we need a GPU cluster for this example, let's pick a *STANDARD_NC6* model and create an AzureML compute.
+In the following example script, we provision a Linux [`compute cluster`](./how-to-create-attach-compute-cluster.md?tabs=python). You can see the [`Azure Machine Learning pricing`](https://azure.microsoft.com/pricing/details/machine-learning/) page for the full list of VM sizes and prices. Since we need a GPU cluster for this example, let's pick a *STANDARD_NC6* model and create an AzureML compute.
 
 [!notebook-python[](~/azureml-examples-main/sdk/python/jobs/single-step/pytorch/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb?name=gpu_compute_target)]
 

articles/machine-learning/how-to-train-scikit-learn.md

@@ -82,7 +82,7 @@ The result of running this script is a workspace handle that you'll use to manag
 
 AzureML needs a compute resource to run a job. This resource can be single or multi-node machines with Linux or Windows OS, or a specific compute fabric like Spark.
 
-In the following example script, we provision a Linux [`compute cluster`](/azure/machine-learning/how-to-create-attach-compute-cluster?tabs=python). You can see the [`Azure Machine Learning pricing`](https://azure.microsoft.com/pricing/details/machine-learning/) page for the full list of VM sizes and prices. We only need a basic cluster for this example; thus, we'll pick a Standard_DS3_v2 model with 2 vCPU cores and 7 GB RAM to create an AzureML compute.
+In the following example script, we provision a Linux [`compute cluster`](./how-to-create-attach-compute-cluster.md?tabs=python). You can see the [`Azure Machine Learning pricing`](https://azure.microsoft.com/pricing/details/machine-learning/) page for the full list of VM sizes and prices. We only need a basic cluster for this example; thus, we'll pick a Standard_DS3_v2 model with 2 vCPU cores and 7 GB RAM to create an AzureML compute.
 
 [!notebook-python[](~/azureml-examples-main/sdk/python/jobs/single-step/scikit-learn/train-hyperparameter-tune-deploy-with-sklearn/train-hyperparameter-tune-with-sklearn.ipynb?name=cpu_compute_target)]
 
@@ -217,4 +217,4 @@ After you've registered your model, you can deploy it the same way as any other
 In this article, you trained and registered a scikit-learn model, and you learned about deployment options. See these other articles to learn more about Azure Machine Learning.
 
 * [Track run metrics during training](how-to-log-view-metrics.md)
-* [Tune hyperparameters](how-to-tune-hyperparameters.md)
+* [Tune hyperparameters](how-to-tune-hyperparameters.md)

articles/machine-learning/how-to-train-tensorflow.md

@@ -90,7 +90,7 @@ The result of running this script is a workspace handle that you'll use to manag
 
 AzureML needs a compute resource to run a job. This resource can be single or multi-node machines with Linux or Windows OS, or a specific compute fabric like Spark.
 
-In the following example script, we provision a Linux [`compute cluster`](/azure/machine-learning/how-to-create-attach-compute-cluster?tabs=python). You can see the [`Azure Machine Learning pricing`](https://azure.microsoft.com/pricing/details/machine-learning/) page for the full list of VM sizes and prices. Since we need a GPU cluster for this example, let's pick a *STANDARD_NC6* model and create an AzureML compute.
+In the following example script, we provision a Linux [`compute cluster`](./how-to-create-attach-compute-cluster.md?tabs=python). You can see the [`Azure Machine Learning pricing`](https://azure.microsoft.com/pricing/details/machine-learning/) page for the full list of VM sizes and prices. Since we need a GPU cluster for this example, let's pick a *STANDARD_NC6* model and create an AzureML compute.
 
 [!notebook-python[](~/azureml-examples-main/sdk/python/jobs/single-step/tensorflow/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb?name=cpu_compute_target)]
 
@@ -277,4 +277,4 @@ In this article, you trained and registered a TensorFlow model. You also deploye
 
 - [Track run metrics during training](how-to-log-view-metrics.md)
 - [Tune hyperparameters](how-to-tune-hyperparameters.md)
-- [Reference architecture for distributed deep learning training in Azure](/azure/architecture/reference-architectures/ai/training-deep-learning)
+- [Reference architecture for distributed deep learning training in Azure](/azure/architecture/reference-architectures/ai/training-deep-learning)

articles/machine-learning/how-to-use-automl-small-object-detect.md

@@ -133,8 +133,8 @@ See the [object detection sample notebook](https://github.com/Azure/azureml-exam
 
 ## Next steps
 
-* Learn more about [how and where to deploy a model](/azure/machine-learning/how-to-deploy-managed-online-endpoints).
+* Learn more about [how and where to deploy a model](./how-to-deploy-managed-online-endpoints.md).
 * For definitions and examples of the performance charts and metrics provided for each job, see [Evaluate automated machine learning experiment results](how-to-understand-automated-ml.md).
 * [Tutorial: Train an object detection model with AutoML and Python](tutorial-auto-train-image-models.md).
 * See [what hyperparameters are available for computer vision tasks](reference-automl-images-hyperparameters.md).
-* [Make predictions with ONNX on computer vision models from AutoML](how-to-inference-onnx-automl-image-models.md)
+* [Make predictions with ONNX on computer vision models from AutoML](how-to-inference-onnx-automl-image-models.md)