fixing broken links

Author: msakande

articles/machine-learning/how-to-use-batch-model-deployments.md

@@ -27,7 +27,7 @@ Use batch endpoints for model deployment when:
 - You don't have low latency requirements.
 - You can take advantage of parallelization.
 
-In this article, you use a batch endpoint to deploy a machine learning model that solves the classic MNIST (Modified National Institute of Standards and Technology) digit recognition problem. Your deployed model then performs batch inferencing over large amounts of data—in this case, image files. You begin by creating a batch deployment of a model that was created using Torch. This deployment becomes the default one in the endpoint. Later, you [create a second deployment](#adding-deployments-to-an-endpoint) of a mode that was created with TensorFlow (Keras), test the second deployment, and then set it as the endpoint's default deployment.
+In this article, you use a batch endpoint to deploy a machine learning model that solves the classic MNIST (Modified National Institute of Standards and Technology) digit recognition problem. Your deployed model then performs batch inferencing over large amounts of data—in this case, image files. You begin by creating a batch deployment of a model that was created using Torch. This deployment becomes the default one in the endpoint. Later, you [create a second deployment](#add-deployments-to-an-endpoint) of a mode that was created with TensorFlow (Keras), test the second deployment, and then set it as the endpoint's default deployment.
 
 To follow along with the code samples and files needed to run the commands in this article locally, see the __[Clone the examples repository](#clone-the-examples-repository)__ section. The code samples and files are contained in the [azureml-examples](https://github.com/azure/azureml-examples) repository.
 
@@ -386,7 +386,7 @@ A model deployment is a set of resources required for hosting the model that doe
     :::code language="azurecli" source="~/azureml-examples-main/cli/endpoints/batch/deploy-models/mnist-classifier/deploy-and-run.sh" ID="create_deployment" :::
 
     > [!TIP]
-    > The `--set-default` parameter sets the newly created deployment as the default deployment of the endpoint. It's a convenient way to create a new default deployment of the endpoint, especially for the first deployment creation. As a best practice for production scenarios, you might want to create a new deployment without setting it as default. Verify that the deployment works as you expect, and then update the default deployment later. For more information on implementing this process, see the [Deploy a new model](#adding-deployments-to-an-endpoint) section.
+    > The `--set-default` parameter sets the newly created deployment as the default deployment of the endpoint. It's a convenient way to create a new default deployment of the endpoint, especially for the first deployment creation. As a best practice for production scenarios, you might want to create a new deployment without setting it as default. Verify that the deployment works as you expect, and then update the default deployment later. For more information on implementing this process, see the [Deploy a new model](#add-deployments-to-an-endpoint) section.
 
     # [Python](#tab/python)