edit 1

Author: rmca14

articles/machine-learning/how-to-image-processing-batch.md

@@ -66,7 +66,7 @@ __endpoint.yml__
 
 Run the following code to create the endpoint.
 
-:::code language="azurecli" source="~/azureml-examples-main/cli/endpoints/batch/deploy-models/imagenet-classifier/deploy-and-run.sh" ID="create_batch_endpoint" :::
+:::code language="azurecli" source="~/azureml-examples-main/cli/endpoints/batch/deploy-models/imagenet-classifier/deploy-and-run.sh" ID="create_endpoint" :::
 
 # [Python](#tab/python)
 
@@ -193,7 +193,7 @@ One the scoring script is created, it's time to create a batch deployment for it
 
    Then, create the deployment with the following command:
 
-   :::code language="azurecli" source="~/azureml-examples-main/cli/endpoints/batch/deploy-models/imagenet-classifier/deploy-and-run.sh" ID="create_batch_deployment_set_default" :::
+   :::code language="azurecli" source="~/azureml-examples-main/cli/endpoints/batch/deploy-models/imagenet-classifier/deploy-and-run.sh" ID="create_deployment" :::
 
    # [Python](#tab/python)
 
@@ -342,7 +342,7 @@ For testing our endpoint, we are going to use a sample of 1000 images from the o
 
    To download the predictions, use the following command:
 
-   :::code language="azurecli" source="~/azureml-examples-main/cli/endpoints/batch/deploy-models/imagenet-classifier/deploy-and-run.sh" ID="download_output" :::
+   :::code language="azurecli" source="~/azureml-examples-main/cli/endpoints/batch/deploy-models/imagenet-classifier/deploy-and-run.sh" ID="download_outputs" :::
 
    # [Python](#tab/python)
 
@@ -398,7 +398,7 @@ On those cases, we may want to perform inference on the entire batch of data. Th
 
    Then, create the deployment with the following command:
 
-   :::code language="azurecli" source="~/azureml-examples-main/cli/endpoints/batch/deploy-models/imagenet-classifier/deploy-and-run.sh" ID="create_batch_deployment_ht" :::
+   :::code language="azurecli" source="~/azureml-examples-main/cli/endpoints/batch/deploy-models/imagenet-classifier/deploy-and-run.sh" ID="create_deployment_ht" :::
 
    # [Python](#tab/python)
 

articles/machine-learning/how-to-nlp-processing-batch.md

@@ -222,7 +222,7 @@ Let's create the deployment that will host the model:
 
    Then, create the deployment with the following command:
 
-   :::code language="azurecli" source="~/azureml-examples-main/cli/endpoints/batch/deploy-models/huggingface-text-summarization/deploy-and-run.sh" ID="create_batch_deployment_set_default" :::
+   :::code language="azurecli" source="~/azureml-examples-main/cli/endpoints/batch/deploy-models/huggingface-text-summarization/deploy-and-run.sh" ID="create_deployment" :::
 
    # [Python](#tab/python)
 

articles/machine-learning/how-to-use-batch-endpoints.md

@@ -97,7 +97,7 @@ A batch endpoint is an HTTPS endpoint that clients can call to trigger a batch i
     
     Run the following code to create a batch deployment under the batch endpoint and set it as the default deployment.
 
-    :::code language="azurecli" source="~/azureml-examples-main/cli/endpoints/batch/deploy-models/mnist-classifier/deploy-and-run.sh" ID="create_batch_endpoint" :::
+    :::code language="azurecli" source="~/azureml-examples-main/cli/endpoints/batch/deploy-models/mnist-classifier/deploy-and-run.sh" ID="create_endpoint" :::
 
     # [Python](#tab/python)
     
@@ -176,7 +176,7 @@ For instance, the following example downloads the output __score__ from the job.
 
 # [Azure CLI](#tab/cli)
 
-:::code language="azurecli" source="~/azureml-examples-main/cli/endpoints/batch/deploy-models/mnist-classifier/deploy-and-run.sh" ID="download_scores" :::
+:::code language="azurecli" source="~/azureml-examples-main/cli/endpoints/batch/deploy-models/mnist-classifier/deploy-and-run.sh" ID="download_outputs" :::
 
 # [Python](#tab/python)
 
@@ -212,7 +212,7 @@ To add a new deployment to an existing endpoint, use the  code:
 
 # [Azure CLI](#tab/cli)
 
-:::code language="azurecli" source="~/azureml-examples-main/cli/endpoints/batch/deploy-models/mnist-classifier/deploy-and-run.sh" ID="create_new_deployment_non_default" :::
+:::code language="azurecli" source="~/azureml-examples-main/cli/endpoints/batch/deploy-models/mnist-classifier/deploy-and-run.sh" ID="create_deployment_non_default" :::
 
 # [Python](#tab/python)