add v2 include

Author: sdgilley

articles/machine-learning/how-to-autoscale-endpoints.md

@@ -34,6 +34,8 @@ To enable autoscale for an endpoint, you first define an autoscale profile. This
 
 # [Azure CLI](#tab/azure-cli)
 
+[!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
+
 The following snippet sets the endpoint and deployment names:
 
 :::code language="azurecli" source="~/azureml-examples-main/cli/deploy-moe-autoscale.sh" ID="set_endpoint_deployment_name" :::
@@ -76,6 +78,8 @@ A common scaling out rule is one that increases the number of VM instances when
 
 # [Azure CLI](#tab/azure-cli)
 
+[!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
+
 :::code language="azurecli" source="~/azureml-examples-main/cli/deploy-moe-autoscale.sh" ID="scale_out_on_cpu_util" :::
 
 The rule is part of the `my-scale-settings` profile (`autoscale-name` matches the `name` of the profile). The value of its `condition` argument says the rule should trigger when "The average CPU consumption among the VM instances exceeds 70% for five minutes." When that condition is satisfied, two more VM instances are allocated. 
@@ -104,6 +108,8 @@ When load is light, a scaling in rule can reduce the number of VM instances. The
 
 # [Azure CLI](#tab/azure-cli)
 
+[!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
+
 :::code language="azurecli" source="~/azureml-examples-main/cli/deploy-moe-autoscale.sh" ID="scale_in_on_cpu_util" :::
 
 # [Portal](#tab/azure-portal)
@@ -131,6 +137,8 @@ The previous rules applied to the deployment. Now, add a rule that applies to th
 
 # [Azure CLI](#tab/azure-cli)
 
+[!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
+
 :::code language="azurecli" source="~/azureml-examples-main/cli/deploy-moe-autoscale.sh" ID="scale_up_on_request_latency" :::
 
 # [Portal](#tab/azure-portal)
@@ -157,6 +165,8 @@ You can also create rules that apply only on certain days or at certain times. I
 
 # [Azure CLI](#tab/azure-cli)
 
+[!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
+
 :::code language="azurecli" source="~/azureml-examples-main/cli/deploy-moe-autoscale.sh" ID="weekend_profile" :::
 
 # [Portal](#tab/azure-portal)

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

@@ -15,6 +15,8 @@ ms.custom: devx-track-azurecli, devplatv2
 
 # Train models with the CLI (v2) (preview)
 
+[!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
+
 The Azure Machine Learning CLI (v2) is an Azure CLI extension enabling you to accelerate the model training process while scaling up and out on Azure compute, with the model lifecycle tracked and auditable.
 
 Training a machine learning model is typically an iterative process. Modern tooling makes it easier than ever to train larger models on more data faster. Previously tedious manual processes like hyperparameter tuning and even algorithm selection are often automated. With the Azure Machine Learning CLI (v2), you can track your jobs (and models) in a [workspace](concept-workspace.md) with hyperparameter sweeps, scale-up on high-performance Azure compute, and scale-out utilizing distributed training.