replace branch in links

Author: sdgilley

articles/machine-learning/concept-azure-machine-learning-v2.md

@@ -68,7 +68,7 @@ ws_basic = Workspace(
 ml_client.workspaces.begin_create(ws_basic) # use MLClient to connect to the subscription and resource group and create workspace
 ```
 
-This [Jupyter notebook](https://github.com/Azure/azureml-examples/blob/v2samplesreorg/sdk/python/resources/workspace/workspace.ipynb) shows more ways to create an Azure ML workspace using SDK v2.
+This [Jupyter notebook](https://github.com/Azure/azureml-examples/blob/main/sdk/python/resources/workspace/workspace.ipynb) shows more ways to create an Azure ML workspace using SDK v2.
 
 ---
 
@@ -113,7 +113,7 @@ cluster_basic = AmlCompute(
 ml_client.begin_create_or_update(cluster_basic)
 ```
 
-This [Jupyter notebook](https://github.com/Azure/azureml-examples/blob/v2samplesreorg/sdk/python/resources/compute/compute.ipynb) shows more ways to create compute using SDK v2.
+This [Jupyter notebook](https://github.com/Azure/azureml-examples/blob/main/sdk/python/resources/compute/compute.ipynb) shows more ways to create compute using SDK v2.
 
 ---
 
@@ -157,7 +157,7 @@ blob_datastore1 = AzureBlobDatastore(
 ml_client.create_or_update(blob_datastore1)
 ```
 
-This [Jupyter notebook](https://github.com/Azure/azureml-examples/blob/v2samplesreorg/sdk/python/resources/datastores/datastore.ipynb) shows more ways to create datastores using SDK v2.
+This [Jupyter notebook](https://github.com/Azure/azureml-examples/blob/main/sdk/python/resources/datastores/datastore.ipynb) shows more ways to create datastores using SDK v2.
 
 ---
 
@@ -250,7 +250,7 @@ my_env = Environment(
 ml_client.environments.create_or_update(my_env) # use the MLClient to connect to workspace and create/register the environment
 ```
 
-This [Jupyter notebook](https://github.com/Azure/azureml-examples/blob/v2samplesreorg/sdk/python/assets/environment/environment.ipynb) shows more ways to create custom environments using SDK v2.
+This [Jupyter notebook](https://github.com/Azure/azureml-examples/blob/main/sdk/python/assets/environment/environment.ipynb) shows more ways to create custom environments using SDK v2.
 
 ---
 

articles/machine-learning/concept-mlflow.md

@@ -94,18 +94,18 @@ 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/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.
+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/v1/notebooks/using-mlflow/readme.md):
+
+* [Training and tracking an XGBoost classifier with MLflow](https://github.com/Azure/azureml-examples/blob/main/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/main/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/main/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/main/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/main/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/main/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/main/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/main/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/main/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/main/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.
 
 ## Next steps
 

articles/machine-learning/how-to-auto-train-forecast.md

@@ -248,7 +248,7 @@ The table shows resulting feature engineering that occurs when window aggregatio
 
 ![target rolling window](./media/how-to-auto-train-forecast/target-roll.svg)
 
-View a Python code example applying the [target rolling window aggregate feature](https://github.com/Azure/azureml-examples/blob/v2samplesreorg/v1/python-sdk/tutorials/automl-with-azureml/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb).
+View a Python code example applying the [target rolling window aggregate feature](https://github.com/Azure/azureml-examples/blob/main/v1/python-sdk/tutorials/automl-with-azureml/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb).
 
 ### Short series handling
 
@@ -295,7 +295,7 @@ best_run, fitted_model = local_run.get_output()
 
 Use the best model iteration to forecast values for data that wasn't used to train the model. 
 
-The [forecast_quantiles()](/python/api/azureml-train-automl-client/azureml.train.automl.model_proxy.modelproxy#forecast-quantiles-x-values--typing-any--y-values--typing-union-typing-any--nonetype----none--forecast-destination--typing-union-typing-any--nonetype----none--ignore-data-errors--bool---false-----azureml-data-abstract-dataset-abstractdataset) function allows specifications of when predictions should start, unlike the `predict()` method, which is typically used for classification and regression tasks. The forecast_quantiles() method by default generates a point forecast or a mean/median forecast which doesn't have a cone of uncertainty around it. Learn more in the [Forecasting away from training data notebook](https://github.com/Azure/azureml-examples/blob/v2samplesreorg/v1/python-sdk/tutorials/automl-with-azureml/forecasting-forecast-function/auto-ml-forecasting-function.ipynb).
+The [forecast_quantiles()](/python/api/azureml-train-automl-client/azureml.train.automl.model_proxy.modelproxy#forecast-quantiles-x-values--typing-any--y-values--typing-union-typing-any--nonetype----none--forecast-destination--typing-union-typing-any--nonetype----none--ignore-data-errors--bool---false-----azureml-data-abstract-dataset-abstractdataset) function allows specifications of when predictions should start, unlike the `predict()` method, which is typically used for classification and regression tasks. The forecast_quantiles() method by default generates a point forecast or a mean/median forecast which doesn't have a cone of uncertainty around it. Learn more in the [Forecasting away from training data notebook](https://github.com/Azure/azureml-examples/blob/main/v1/python-sdk/tutorials/automl-with-azureml/forecasting-forecast-function/auto-ml-forecasting-function.ipynb).
 
 In the following example, you first replace all values in `y_pred` with `NaN`. The forecast origin is at the end of training data in this case. However, if you replaced only the second half of `y_pred` with `NaN`, the function would leave the numerical values in the first half unmodified, but forecast the `NaN` values in the second half. The function returns both the forecasted values and the aligned features.
 
@@ -317,7 +317,7 @@ fitted_model.forecast_quantiles(
     test_dataset, label_query, forecast_destination=pd.Timestamp(2019, 1, 8))
 ```
 
-You can calculate model metrics like, root mean squared error (RMSE) or mean absolute percentage error (MAPE) to help you estimate the models performance. See the Evaluate section of the [Bike share demand notebook](https://github.com/Azure/azureml-examples/blob/v2samplesreorg/v1/python-sdk/tutorials/automl-with-azureml/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb) for an example. 
+You can calculate model metrics like, root mean squared error (RMSE) or mean absolute percentage error (MAPE) to help you estimate the models performance. See the Evaluate section of the [Bike share demand notebook](https://github.com/Azure/azureml-examples/blob/main/v1/python-sdk/tutorials/automl-with-azureml/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb) for an example. 
 
 After the overall model accuracy has been determined, the most realistic next step is to use the model to forecast unknown future values. 
 
@@ -349,7 +349,7 @@ The following diagram shows the workflow for the many models solution.
 
 ![Many models concept diagram](./media/how-to-auto-train-forecast/many-models.svg)
 
-The following code demonstrates the key parameters users need to set up their many models run. See the [Many Models- Automated ML notebook](https://github.com/Azure/azureml-examples/blob/v2samplesreorg/v1/python-sdk/tutorials/automl-with-azureml/forecasting-many-models/auto-ml-forecasting-many-models.ipynb) for a many models forecasting example 
+The following code demonstrates the key parameters users need to set up their many models run. See the [Many Models- Automated ML notebook](https://github.com/Azure/azureml-examples/blob/main/v1/python-sdk/tutorials/automl-with-azureml/forecasting-many-models/auto-ml-forecasting-many-models.ipynb) for a many models forecasting example 
 
 ```python
 from azureml.train.automl.runtime._many_models.many_models_parameters import ManyModelsTrainParameters
@@ -386,7 +386,7 @@ To further visualize this, the leaf levels of the hierarchy contain all the time
 
 The hierarchical time series solution is built on top of the Many Models Solution and share a similar configuration setup.
 
-The following code demonstrates the key parameters to set up your hierarchical time series forecasting runs. See the [Hierarchical time series- Automated ML notebook](https://github.com/Azure/azureml-examples/blob/v2samplesreorg/v1/python-sdk/tutorials/automl-with-azureml/forecasting-hierarchical-timeseries/auto-ml-forecasting-hierarchical-timeseries.ipynb), for an end to end example. 
+The following code demonstrates the key parameters to set up your hierarchical time series forecasting runs. See the [Hierarchical time series- Automated ML notebook](https://github.com/Azure/azureml-examples/blob/main/v1/python-sdk/tutorials/automl-with-azureml/forecasting-hierarchical-timeseries/auto-ml-forecasting-hierarchical-timeseries.ipynb), for an end to end example. 
 
 ```python
 
@@ -430,10 +430,10 @@ hts_parameters = HTSTrainParameters(
 
 See the [forecasting sample notebooks](https://github.com/Azure/azureml-examples/tree/main/v1/python-sdk/tutorials/automl-with-azureml) for detailed code examples of advanced forecasting configuration including:
 
-* [holiday detection and featurization](https://github.com/Azure/azureml-examples/blob/v2samplesreorg/v1/python-sdk/tutorials/automl-with-azureml/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb)
-* [rolling-origin cross validation](https://github.com/Azure/azureml-examples/blob/v2samplesreorg/v1/python-sdk/tutorials/automl-with-azureml/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb)
-* [configurable lags](https://github.com/Azure/azureml-examples/blob/v2samplesreorg/v1/python-sdk/tutorials/automl-with-azureml/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb)
-* [rolling window aggregate features](https://github.com/Azure/azureml-examples/blob/v2samplesreorg/v1/python-sdk/tutorials/automl-with-azureml/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb)
+* [holiday detection and featurization](https://github.com/Azure/azureml-examples/blob/main/v1/python-sdk/tutorials/automl-with-azureml/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb)
+* [rolling-origin cross validation](https://github.com/Azure/azureml-examples/blob/main/v1/python-sdk/tutorials/automl-with-azureml/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb)
+* [configurable lags](https://github.com/Azure/azureml-examples/blob/main/v1/python-sdk/tutorials/automl-with-azureml/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb)
+* [rolling window aggregate features](https://github.com/Azure/azureml-examples/blob/main/v1/python-sdk/tutorials/automl-with-azureml/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb)
 
 
 ## Next steps