azureml-examples/blob/main/notebooks/

Author: sdgilley

articles/machine-learning/concept-mlflow.md

@@ -99,15 +99,15 @@ The following table shows which operations are supported by each of the tools av
 
 ## Example notebooks
 
-If you're getting started with MLflow in Azure Machine Learning, we recommend that you explore the [notebook examples about how to use MLflow](https://github.com/Azure/azureml-examples/blob/main/notebooks/using-mlflow/readme.md):
-
-* [Training and tracking an XGBoost classifier with MLflow](https://github.com/Azure/azureml-examples/blob/main/notebooks/using-mlflow/train-with-mlflow/xgboost_classification_mlflow.ipynb): Demonstrates how to track experiments by using MLflow, log models, and combine multiple flavors into pipelines.
-* [Training and tracking an XGBoost classifier with MLflow using service principal authentication](https://github.com/Azure/azureml-examples/blob/main/notebooks/using-mlflow/train-with-mlflow/xgboost_service_principal.ipynb): Demonstrates how to track experiments by using MLflow from compute that's running outside Azure Machine Learning. It shows how to authenticate against Azure Machine Learning services by using a service principal.
-* [Hyper-parameter optimization using Hyperopt and nested runs in MLflow](https://github.com/Azure/azureml-examples/blob/main/notebooks/using-mlflow/train-with-mlflow/xgboost_nested_runs.ipynb): Demonstrates how to use child runs in MLflow to do hyper-parameter optimization for models by using the popular library Hyperopt. It shows how to transfer metrics, parameters, and artifacts from child runs to parent runs.
-* [Logging models with MLflow](https://github.com/Azure/azureml-examples/blob/main/notebooks/using-mlflow/logging-models/logging_model_with_mlflow.ipynb): Demonstrates how to use the concept of models instead of artifacts with MLflow, including how to construct custom models.
-* [Manage runs and experiments with MLflow](https://github.com/Azure/azureml-examples/blob/main/notebooks/using-mlflow/run-history/run_history.ipynb): Demonstrates how to query experiments, runs, metrics, parameters, and artifacts from Azure Machine Learning by using MLflow.
-* [Manage model registries with MLflow](https://github.com/Azure/azureml-examples/blob/main/notebooks/using-mlflow/model-management/model_management.ipynb): Demonstrates how to manage models in registries by using MLflow.
-* [Deploying models with MLflow](https://github.com/Azure/azureml-examples/blob/main/notebooks/using-mlflow/no-code-deployment/deploying_with_mlflow.ipynb): Demonstrates how to deploy no-code models in MLflow format to a deployment target in Azure Machine Learning.
-* [Training models in Azure Databricks and deploying them on Azure Machine Learning](https://github.com/Azure/azureml-examples/blob/main/notebooks/using-mlflow/no-code-deployment/track_with_databricks_deploy_aml.ipynb): Demonstrates how to train models in Azure Databricks and deploy them in Azure Machine Learning. It also includes how to handle cases where you also want to track the experiments with the MLflow instance in Azure Databricks.
-* [Migrating models with a scoring script to MLflow](https://github.com/Azure/azureml-examples/blob/main/notebooks/using-mlflow/migrating-scoring-to-mlflow/scoring_to_mlmodel.ipynb): Demonstrates how to migrate models with scoring scripts to no-code deployment with MLflow.
-* [Using MLflow REST with Azure Machine Learning](https://github.com/Azure/azureml-examples/blob/main/notebooks/using-mlflow/using-rest-api/using_mlflow_rest_api.ipynb): Demonstrates how to work with the MLflow REST API when you're connected to Azure Machine Learning.
+If you're getting started with MLflow in Azure Machine Learning, we recommend that you explore the [notebook examples about how to use MLflow](https://github.com/Azure/azureml-examples/blob/v2samplesreorg/v1/notebooks/using-mlflow/readme.md):
+
+* [Training and tracking an XGBoost classifier with MLflow](https://github.com/Azure/azureml-examples/blob/v2samplesreorg/v1/notebooks/using-mlflow/train-with-mlflow/xgboost_classification_mlflow.ipynb): Demonstrates how to track experiments by using MLflow, log models, and combine multiple flavors into pipelines.
+* [Training and tracking an XGBoost classifier with MLflow using service principal authentication](https://github.com/Azure/azureml-examples/blob/v2samplesreorg/v1/notebooks/using-mlflow/train-with-mlflow/xgboost_service_principal.ipynb): Demonstrates how to track experiments by using MLflow from compute that's running outside Azure Machine Learning. It shows how to authenticate against Azure Machine Learning services by using a service principal.
+* [Hyper-parameter optimization using Hyperopt and nested runs in MLflow](https://github.com/Azure/azureml-examples/blob/v2samplesreorg/v1/notebooks/using-mlflow/train-with-mlflow/xgboost_nested_runs.ipynb): Demonstrates how to use child runs in MLflow to do hyper-parameter optimization for models by using the popular library Hyperopt. It shows how to transfer metrics, parameters, and artifacts from child runs to parent runs.
+* [Logging models with MLflow](https://github.com/Azure/azureml-examples/blob/v2samplesreorg/v1/notebooks/using-mlflow/logging-models/logging_model_with_mlflow.ipynb): Demonstrates how to use the concept of models instead of artifacts with MLflow, including how to construct custom models.
+* [Manage runs and experiments with MLflow](https://github.com/Azure/azureml-examples/blob/v2samplesreorg/v1/notebooks/using-mlflow/run-history/run_history.ipynb): Demonstrates how to query experiments, runs, metrics, parameters, and artifacts from Azure Machine Learning by using MLflow.
+* [Manage model registries with MLflow](https://github.com/Azure/azureml-examples/blob/v2samplesreorg/v1/notebooks/using-mlflow/model-management/model_management.ipynb): Demonstrates how to manage models in registries by using MLflow.
+* [Deploying models with MLflow](https://github.com/Azure/azureml-examples/blob/v2samplesreorg/v1/notebooks/using-mlflow/no-code-deployment/deploying_with_mlflow.ipynb): Demonstrates how to deploy no-code models in MLflow format to a deployment target in Azure Machine Learning.
+* [Training models in Azure Databricks and deploying them on Azure Machine Learning](https://github.com/Azure/azureml-examples/blob/v2samplesreorg/v1/notebooks/using-mlflow/no-code-deployment/track_with_databricks_deploy_aml.ipynb): Demonstrates how to train models in Azure Databricks and deploy them in Azure Machine Learning. It also includes how to handle cases where you also want to track the experiments with the MLflow instance in Azure Databricks.
+* [Migrating models with a scoring script to MLflow](https://github.com/Azure/azureml-examples/blob/v2samplesreorg/v1/notebooks/using-mlflow/migrating-scoring-to-mlflow/scoring_to_mlmodel.ipynb): Demonstrates how to migrate models with scoring scripts to no-code deployment with MLflow.
+* [Using MLflow REST with Azure Machine Learning](https://github.com/Azure/azureml-examples/blob/v2samplesreorg/v1/notebooks/using-mlflow/using-rest-api/using_mlflow_rest_api.ipynb): Demonstrates how to work with the MLflow REST API when you're connected to Azure Machine Learning.

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

@@ -52,7 +52,7 @@ 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/notebooks/using-mlflow/train-with-mlflow/xgboost_classification_mlflow.ipynb) demostrates 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/v2samplesreorg/v1/notebooks/using-mlflow/train-with-mlflow/xgboost_classification_mlflow.ipynb) demostrates 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

@@ -241,8 +241,8 @@ child_runs = mlflow.search_runs(
 
 The [MLflow with Azure ML notebooks](https://github.com/Azure/azureml-examples/tree/master/notebooks/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/notebooks/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/notebooks/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/v2samplesreorg/v1/notebooks/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/v2samplesreorg/v1/notebooks/using-mlflow/run-history/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

@@ -238,7 +238,7 @@ 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/notebooks/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/v2samplesreorg/v1/notebooks/using-mlflow/no-code-deployment/track_with_databricks_deploy_aml.ipynb).
 
 ## Deploying and consuming models registered in Azure Machine Learning
 
@@ -252,7 +252,7 @@ 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/notebooks/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/v2samplesreorg/v1/notebooks/using-mlflow/no-code-deployment/track_with_databricks_deploy_aml.ipynb)
 
 ### Deploy models to ADB for batch scoring using UDFs
 
@@ -295,7 +295,7 @@ If you don't plan to use the logged metrics and artifacts in your workspace, the
 
 ## Example notebooks
 
-The [Training models in Azure Databricks and deploying them on Azure ML](https://github.com/Azure/azureml-examples/blob/main/notebooks/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/v2samplesreorg/v1/notebooks/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.
 
 ## Next steps
 * [Deploy MLflow models as an Azure web service](how-to-deploy-mlflow-models.md).