removing merged include; it was linking from v1 to v2 content, which we should not be doing.

Author: Blackmist

articles/machine-learning/v1/how-to-monitor-tensorboard.md

@@ -141,9 +141,6 @@ compute_target.wait_for_completion(show_output=True, min_node_count=None)
 # print(compute_target.get_status().serialize())
 ```
 
-> [!NOTE]
-> You may choose to use [low-priority VMs](../how-to-manage-optimize-cost.md#low-pri-vm) to run some or all of your workloads. See how to [create a low-priority VM](how-to-create-attach-compute-cluster.md#lower-your-compute-cluster-cost-with-low-priority-vms). 
-
 ### Configure and submit training job
 
 Configure a training job by creating a ScriptRunConfig object.

articles/machine-learning/v1/how-to-secure-training-vnet.md

@@ -278,9 +278,6 @@ except ComputeTargetException:
 
 When the creation process finishes, you train your model. For more information, see [Select and use a compute target for training](how-to-set-up-training-targets.md).
 
-> [!NOTE]
-> You may choose to use [low-priority VMs](../how-to-manage-optimize-cost.md#low-pri-vm) to run some or all of your workloads. See how to [create a low-priority VM](how-to-create-attach-compute-cluster.md#lower-your-compute-cluster-cost-with-low-priority-vms). 
-
 ## Azure Databricks
 
 * The virtual network must be in the same subscription and region as the Azure Machine Learning workspace.

articles/machine-learning/v1/how-to-train-keras.md

@@ -123,9 +123,6 @@ except ComputeTargetException:
     compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)
 ```
 
-> [!NOTE]
-> You may choose to use [low-priority VMs](../how-to-manage-optimize-cost.md#low-pri-vm) to run some or all of your workloads. See how to [create a low-priority VM](how-to-create-attach-compute-cluster.md#lower-your-compute-cluster-cost-with-low-priority-vms). 
-
 For more information on compute targets, see the [what is a compute target](../concept-compute-target.md) article.
 
 ### Define your environment

articles/machine-learning/v1/how-to-train-pytorch.md

@@ -117,9 +117,6 @@ except ComputeTargetException:
 
 If you instead want to create a CPU cluster, provide a different VM size to the vm_size parameter, such as STANDARD_D2_V2.
 
-> [!NOTE]
-> You may choose to use [low-priority VMs](../how-to-manage-optimize-cost.md#low-pri-vm) to run some or all of your workloads. See how to [create a low-priority VM](how-to-create-attach-compute-cluster.md#lower-your-compute-cluster-cost-with-low-priority-vms). 
-
 For more information on compute targets, see the [what is a compute target](../concept-compute-target.md) article.
 
 ### Define your environment

articles/machine-learning/v1/how-to-train-tensorflow.md

@@ -123,9 +123,6 @@ except ComputeTargetException:
     compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)
 ```
 
-> [!NOTE]
-> You may choose to use [low-priority VMs](../how-to-manage-optimize-cost.md#low-pri-vm) to run some or all of your workloads. See how to [create a low-priority VM](how-to-create-attach-compute-cluster.md#lower-your-compute-cluster-cost-with-low-priority-vms).
-
 For more information on compute targets, see the [what is a compute target](../concept-compute-target.md) article.
 
 ### Define your environment

articles/machine-learning/v1/how-to-use-automlstep-in-pipelines.md

@@ -94,9 +94,6 @@ if not compute_name in ws.compute_targets :
 compute_target = ws.compute_targets[compute_name]
 ```
 
-> [!NOTE]
-> You may choose to use [low-priority VMs](../how-to-manage-optimize-cost.md#low-pri-vm) to run some or all of your workloads. See how to [create a low-priority VM](how-to-create-attach-compute-cluster.md#lower-your-compute-cluster-cost-with-low-priority-vms).
-
 The intermediate data between the data preparation and the automated ML step can be stored in the workspace's default datastore, so we don't need to do more than call `get_default_datastore()` on the `Workspace` object. 
 
 After that, the code checks if the Azure Machine Learning compute target `'cpu-cluster'` already exists. If not, we specify that we want a small CPU-based compute target. If you plan to use automated ML's deep learning features (for instance, text featurization with DNN support) you should choose a compute with strong GPU support, as described in [GPU optimized virtual machine sizes](../../virtual-machines/sizes-gpu.md).