Merge pull request #225666 from pritamso/Broken-link-fix-santiagxf

Broken link fix

Author: prmerger-automator[bot]

articles/machine-learning/concept-mlflow.md

@@ -80,7 +80,7 @@ Learn more at [Guidelines for deploying MLflow models](how-to-deploy-mlflow-mode
 * [Deploy MLflow to Online Endpoints](https://github.com/Azure/azureml-examples/blob/main/sdk/python/using-mlflow/deploy/mlflow_sdk_online_endpoints.ipynb): Demonstrates how to deploy models in MLflow format to online endpoints using MLflow SDK.
 * [Deploy MLflow to Online Endpoints with safe rollout](https://github.com/Azure/azureml-examples/blob/main/sdk/python/using-mlflow/deploy/mlflow_sdk_online_endpoints_progresive.ipynb): Demonstrates how to deploy models in MLflow format to online endpoints using MLflow SDK with progressive rollout of models and the deployment of multiple model's versions in the same endpoint.
 * [Deploy MLflow to web services (V1)](https://github.com/Azure/azureml-examples/blob/main/sdk/python/using-mlflow/deploy/mlflow_sdk_web_service.ipynb): Demonstrates how to deploy models in MLflow format to web services (ACI/AKS v1) using MLflow SDK.
-* [Deploying models trained in Azure Databricks to Azure Machine Learning with MLflow](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 with the MLflow instance in Azure Databricks.
+* [Deploying models trained in Azure Databricks to Azure Machine Learning with MLflow](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 with the MLflow instance in Azure Databricks.
 
 ## Training MLflow projects (preview)
 

articles/machine-learning/how-to-log-mlflow-models.md

@@ -54,7 +54,7 @@ accuracy = accuracy_score(y_test, y_pred)
 ```
 
 > [!TIP]
-> If you are using Machine Learning pipelines, like for instance [Scikit-Learn pipelines](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html), use the `autolog` functionality of that flavor for logging models. Models are automatically logged when the `fit()` method is called on the pipeline object. The notebook [Training and tracking an XGBoost classifier with MLflow](https://github.com/Azure/azureml-examples/blob/main/sdk/python/using-mlflow/train-with-mlflow/xgboost_classification_mlflow.ipynb) demonstrates how to log a model with preprocessing using pipelines.
+> If you are using Machine Learning pipelines, like for instance [Scikit-Learn pipelines](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html), use the `autolog` functionality of that flavor for logging models. Models are automatically logged when the `fit()` method is called on the pipeline object. The notebook [Training and tracking an XGBoost classifier with MLflow](https://github.com/Azure/azureml-examples/blob/main/sdk/python/using-mlflow/train-and-log/xgboost_classification_mlflow.ipynb) demonstrates how to log a model with preprocessing using pipelines.
 
 ## Logging models with a custom signature, environment or samples
 

articles/machine-learning/how-to-track-experiments-mlflow.md

@@ -260,8 +260,8 @@ To compare and evaluate the quality of your jobs and models in AzureML Studio, u
 
 The [MLflow with Azure ML notebooks](https://github.com/Azure/azureml-examples/tree/main/sdk/python/using-mlflow) demonstrate and expand upon concepts presented in this article.
 
-  * [Training and tracking a classifier with MLflow](https://github.com/Azure/azureml-examples/blob/main/sdk/python/using-mlflow/train-with-mlflow/xgboost_classification_mlflow.ipynb): Demonstrates how to track experiments using MLflow, log models and combine multiple flavors into pipelines.
-  * [Manage experiments and runs with MLflow](https://github.com/Azure/azureml-examples/blob/main/sdk/python/using-mlflow/run-history/run_history.ipynb): Demonstrates how to query experiments, runs, metrics, parameters and artifacts from Azure ML using MLflow.
+  * [Training and tracking a classifier with MLflow](https://github.com/Azure/azureml-examples/blob/main/sdk/python/using-mlflow/train-and-log/xgboost_classification_mlflow.ipynb): Demonstrates how to track experiments using MLflow, log models and combine multiple flavors into pipelines.
+  * [Manage experiments and runs with MLflow](https://github.com/Azure/azureml-examples/blob/main/sdk/python/using-mlflow/runs-management/run_history.ipynb): Demonstrates how to query experiments, runs, metrics, parameters and artifacts from Azure ML using MLflow.
 
 
 ## Support matrix for querying runs and experiments

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