fix bookmarks

Author: sdgilley

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

@@ -125,7 +125,7 @@ Compute instance comes with P10 OS disk. Temp disk type depends on the VM size c
 
 ## Compute target
 
-Compute instances can be used as a [training compute target](concept-compute-target.md#train) similar to Azure Machine Learning [compute training clusters](how-to-create-attach-compute-cluster.md).  But a compute instance has only a single node, while a compute cluster can have more nodes.
+Compute instances can be used as a [training compute target](concept-compute-target.md#training-compute-targets) similar to Azure Machine Learning [compute training clusters](how-to-create-attach-compute-cluster.md).  But a compute instance has only a single node, while a compute cluster can have more nodes.
 
 A compute instance:
 

articles/machine-learning/how-to-create-manage-compute-instance.md

@@ -23,7 +23,7 @@ ms.date: 10/19/2022
 
 Learn how to create and manage a [compute instance](concept-compute-instance.md) in your Azure Machine Learning workspace. 
 
-Use a compute instance as your fully configured and managed development environment in the cloud. For development and testing, you can also use the instance as a [training compute target](concept-compute-target.md#train).   A compute instance can run multiple jobs in parallel and has a job queue. As a development environment, a compute instance can't be shared with other users in your workspace.
+Use a compute instance as your fully configured and managed development environment in the cloud. For development and testing, you can also use the instance as a [training compute target](concept-compute-target.md#training-compute-targets).   A compute instance can run multiple jobs in parallel and has a job queue. As a development environment, a compute instance can't be shared with other users in your workspace.
 
 In this article, you learn how to:
 

articles/machine-learning/how-to-customize-compute-instance.md

@@ -22,7 +22,7 @@ ms.date: 05/04/2022
 
 Use a setup script for an automated way to customize and configure a compute instance at provisioning time. 
 
-Use a compute instance as your fully configured and managed development environment in the cloud. For development and testing, you can also use the instance as a [training compute target](concept-compute-target.md#train) or for an [inference target](concept-compute-target.md#deploy).   A compute instance can run multiple jobs in parallel and has a job queue. As a development environment, a compute instance can't be shared with other users in your workspace.
+Use a compute instance as your fully configured and managed development environment in the cloud. For development and testing, you can also use the instance as a [training compute target](concept-compute-target.md#training-compute-targets) or for an [inference target](concept-compute-target.md#compute-targets-for-inference).   A compute instance can run multiple jobs in parallel and has a job queue. As a development environment, a compute instance can't be shared with other users in your workspace.
 
 As an administrator, you can write a customization script to be used to provision all compute instances in the workspace according to your requirements.
 

articles/machine-learning/how-to-manage-resources-vscode.md

@@ -267,7 +267,7 @@ Alternatively, use the `Azure ML: Delete Compute instance` command in the comman
 
 ## Compute clusters
 
-For more information, see [training compute targets](concept-compute-target.md#train).
+For more information, see [training compute targets](concept-compute-target.md#training-compute-targets).
 
 ### Create compute cluster
 
@@ -300,7 +300,7 @@ Alternatively, use the `> Azure ML: Remove Compute` command in the command palet
 
 ## Inference Clusters
 
-For more information, see [compute targets for inference](concept-compute-target.md#deploy).
+For more information, see [compute targets for inference](concept-compute-target.md#compute-targets-for-inference).
 
 ### Manage inference clusters
 

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

@@ -58,7 +58,7 @@ Azure Machine Learning introduces two fully managed cloud-based virtual machines
 
 * **Compute clusters**: Compute clusters are a cluster of VMs with multi-node scaling capabilities. Compute clusters are better suited for compute targets for large jobs and production.  The cluster scales up automatically when a job is submitted.  Use as a training compute target or for dev/test deployment.
 
-For more information about training compute targets, see [Training compute targets](../concept-compute-target.md#train).  For more information about deployment compute targets, see [Deployment targets](../concept-compute-target.md#deploy).
+For more information about training compute targets, see [Training compute targets](../concept-compute-target.md#training-compute-targets).  For more information about deployment compute targets, see [Deployment targets](../concept-compute-target.md#compute-targets-for-inference).
 
 ## Datasets and datastores
 

articles/machine-learning/v1/how-to-create-machine-learning-pipelines.md

@@ -109,7 +109,7 @@ output_data_dataset = output_data1.register_on_complete(name = 'prepared_output_
 ## Set up a compute target
 
 
-In Azure Machine Learning, the term __compute__ (or __compute target__) refers to the machines or clusters that do the computational steps in your machine learning pipeline.   See [compute targets for model training](../concept-compute-target.md#train) for a full list of compute targets and [Create compute targets](../how-to-create-attach-compute-studio.md) for how to create and attach them to your workspace.   The process for creating and or attaching a compute target is the same whether you're training a model or running a pipeline step. After you create and attach your compute target, use the `ComputeTarget` object in your [pipeline step](#steps).
+In Azure Machine Learning, the term __compute__ (or __compute target__) refers to the machines or clusters that do the computational steps in your machine learning pipeline.   See [compute targets for model training](../concept-compute-target.md#training-compute-targets) for a full list of compute targets and [Create compute targets](../how-to-create-attach-compute-studio.md) for how to create and attach them to your workspace.   The process for creating and or attaching a compute target is the same whether you're training a model or running a pipeline step. After you create and attach your compute target, use the `ComputeTarget` object in your [pipeline step](#steps).
 
 > [!IMPORTANT]
 > Performing management operations on compute targets isn't supported from inside remote jobs. Since machine learning pipelines are submitted as a remote job, do not use management operations on compute targets from inside the pipeline.

articles/machine-learning/v1/how-to-create-manage-compute-instance.md

@@ -23,7 +23,7 @@ ms.date: 05/02/2022
 
 Learn how to create and manage a [compute instance](../concept-compute-instance.md) in your Azure Machine Learning workspace with CLI v1.
 
-Use a compute instance as your fully configured and managed development environment in the cloud. For development and testing, you can also use the instance as a [training compute target](../concept-compute-target.md#train) or for an [inference target](../concept-compute-target.md#deploy).   A compute instance can run multiple jobs in parallel and has a job queue. As a development environment, a compute instance can't be shared with other users in your workspace.
+Use a compute instance as your fully configured and managed development environment in the cloud. For development and testing, you can also use the instance as a [training compute target](../concept-compute-target.md#training-compute-targets) or for an [inference target](../concept-compute-target.md#compute-targets-for-inference).   A compute instance can run multiple jobs in parallel and has a job queue. As a development environment, a compute instance can't be shared with other users in your workspace.
 
 Compute instances can run jobs securely in a [virtual network environment](../how-to-secure-training-vnet.md), without requiring enterprises to open up SSH ports. The job executes in a containerized environment and packages your model dependencies in a Docker container.