Update how-to-use-mlflow-azure-databricks.md

Author: pritamso

articles/machine-learning/how-to-use-mlflow-azure-databricks.md

@@ -33,7 +33,7 @@ In this article, you will learn:
 
 ### Example notebooks
 
-The [Training models in Azure Databricks and deploying them on Azure ML](https://github.com/Azure/azureml-examples/blob/main/sdk/python/using-mlflow/no-code-deployment/track_with_databricks_deploy_aml.ipynb) demonstrates how to train models in Azure Databricks and deploy them in Azure ML. It also includes how to handle cases where you also want to track the experiments and models with the MLflow instance in Azure Databricks and leverage Azure ML for deployment.
+The [Training models in Azure Databricks and deploying them on Azure ML](https://github.com/Azure/azureml-examples/blob/main/sdk/python/using-mlflow/deploy/track_with_databricks_deploy_aml.ipynb) demonstrates how to train models in Azure Databricks and deploy them in Azure ML. It also includes how to handle cases where you also want to track the experiments and models with the MLflow instance in Azure Databricks and leverage Azure ML for deployment.
 
 ## Install libraries
 
@@ -286,7 +286,7 @@ mlflow.set_registry_uri(azureml_mlflow_uri)
 > [!NOTE]
 > The value of `azureml_mlflow_uri` was obtained in the same way it was demostrated in [Set MLflow Tracking to only track in your Azure Machine Learning workspace](#tracking-exclusively-on-azure-machine-learning-workspace)
 
-For a complete example about this scenario please check the example [Training models in Azure Databricks and deploying them on Azure ML](https://github.com/Azure/azureml-examples/blob/main/sdk/python/using-mlflow/no-code-deployment/track_with_databricks_deploy_aml.ipynb).
+For a complete example about this scenario please check the example [Training models in Azure Databricks and deploying them on Azure ML](https://github.com/Azure/azureml-examples/blob/main/sdk/python/using-mlflow/deploy/track_with_databricks_deploy_aml.ipynb).
 
 ## Deploying and consuming models registered in Azure Machine Learning
 
@@ -300,7 +300,7 @@ Models registered in Azure Machine Learning Service using MLflow can be consumed
 You can leverage the `azureml-mlflow` plugin to deploy a model to your Azure Machine Learning workspace. Check [How to deploy MLflow models](how-to-deploy-mlflow-models.md) page for a complete detail about how to deploy models to the different targets.
 
 > [!IMPORTANT]
-> Models need to be registered in Azure Machine Learning registry in order to deploy them. If your models happen to be registered in the MLflow instance inside Azure Databricks, you will have to register them again in Azure Machine Learning. If this is you case, please check the example [Training models in Azure Databricks and deploying them on Azure ML](https://github.com/Azure/azureml-examples/blob/main/sdk/python/using-mlflow/no-code-deployment/track_with_databricks_deploy_aml.ipynb)
+> Models need to be registered in Azure Machine Learning registry in order to deploy them. If your models happen to be registered in the MLflow instance inside Azure Databricks, you will have to register them again in Azure Machine Learning. If this is you case, please check the example [Training models in Azure Databricks and deploying them on Azure ML](https://github.com/Azure/azureml-examples/blob/main/sdk/python/using-mlflow/deploy/track_with_databricks_deploy_aml.ipynb)
 
 ### Deploy models to ADB for batch scoring using UDFs