Merge pull request #214728 from sdgilley/sdg-replace-v2samplesrepo

[BULK] switch back to main branch for azureml-examples

Author: tamarakhader

articles/machine-learning/concept-automated-ml.md

@@ -191,7 +191,7 @@ See the [AutoML package](/python/api/azure-ai-ml/azure.ai.ml.automl) for changin
 
 With Azure Machine Learning, you can use automated ML to build a Python model and have it converted to the ONNX format. Once the models are in the ONNX format, they can be run on a variety of platforms and devices. Learn more about [accelerating ML models with ONNX](concept-onnx.md).
 
-See how to convert to ONNX format [in this Jupyter notebook example](https://github.com/Azure/azureml-examples/tree/v2samplesreorg/v1/python-sdk/tutorials/automl-with-azureml/classification-bank-marketing-all-features). Learn which [algorithms are supported in ONNX](how-to-configure-auto-train.md#supported-algorithms).
+See how to convert to ONNX format [in this Jupyter notebook example](https://github.com/Azure/azureml-examples/tree/main/v1/python-sdk/tutorials/automl-with-azureml/classification-bank-marketing-all-features). Learn which [algorithms are supported in ONNX](how-to-configure-auto-train.md#supported-algorithms).
 
 The ONNX runtime also supports C#, so you can use the model built automatically in your C# apps without any need for recoding or any of the network latencies that REST endpoints introduce. Learn more about [using an AutoML ONNX model in a .NET application with ML.NET](./how-to-use-automl-onnx-model-dotnet.md) and [inferencing ONNX models with the ONNX runtime C# API](https://onnxruntime.ai/docs/api/csharp-api.html). 
 

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-component.md

@@ -63,4 +63,4 @@ To learn more about how to build a component, see:
 - [Component CLI v2 YAML reference](./reference-yaml-component-command.md).
 - [What is Azure Machine Learning Pipeline?](concept-ml-pipelines.md).
 - Try out [CLI v2 component example](https://github.com/Azure/azureml-examples/tree/sdk-preview/cli/jobs/pipelines-with-components).
-- Try out [Python SDK v2 component example](https://github.com/Azure/azureml-examples/tree/v2samplesreorg/sdk/python/jobs/pipelines).
+- Try out [Python SDK v2 component example](https://github.com/Azure/azureml-examples/tree/main/sdk/python/jobs/pipelines).

articles/machine-learning/concept-ml-pipelines.md

@@ -67,4 +67,4 @@ Azure Machine Learning pipelines are a powerful facility that begins delivering
 + [Define pipelines with the Azure ML SDK v2](./how-to-create-component-pipeline-python.md)
 + [Define pipelines with Designer](./how-to-create-component-pipelines-ui.md)
 + Try out [CLI v2 pipeline example](https://github.com/Azure/azureml-examples/tree/sdk-preview/cli/jobs/pipelines-with-components)
-+ Try out [Python SDK v2 pipeline example](https://github.com/Azure/azureml-examples/tree/v2samplesreorg/sdk/python/jobs/pipelines)
++ Try out [Python SDK v2 pipeline example](https://github.com/Azure/azureml-examples/tree/main/sdk/python/jobs/pipelines)

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-attach-kubernetes-anywhere.md

@@ -106,5 +106,5 @@ For any AzureML example, you only need to update the compute target name to your
 * Explore training job samples with CLI v2 - [https://github.com/Azure/azureml-examples/tree/main/cli/jobs](https://github.com/Azure/azureml-examples/tree/main/cli/jobs)
 * Explore model deployment with online endpoint samples with CLI v2 - [https://github.com/Azure/azureml-examples/tree/main/cli/endpoints/online/kubernetes](https://github.com/Azure/azureml-examples/tree/main/cli/endpoints/online/kubernetes)
 * Explore batch endpoint samples with CLI v2 - [https://github.com/Azure/azureml-examples/tree/main/cli/endpoints/batch](https://github.com/Azure/azureml-examples/tree/main/cli/endpoints/batch)
-* Explore training job samples with SDK v2 -[https://github.com/Azure/azureml-examples/tree/v2samplesreorg/sdk/python/jobs](https://github.com/Azure/azureml-examples/tree/v2samplesreorg/sdk/python/jobs)
-* Explore model deployment with online endpoint samples with SDK v2 -[https://github.com/Azure/azureml-examples/tree/v2samplesreorg/sdk/python/endpoints/online/kubernetes](https://github.com/Azure/azureml-examples/tree/v2samplesreorg/sdk/python/endpoints/online/kubernetes)
+* Explore training job samples with SDK v2 -[https://github.com/Azure/azureml-examples/tree/main/sdk/python/jobs](https://github.com/Azure/azureml-examples/tree/main/sdk/python/jobs)
+* Explore model deployment with online endpoint samples with SDK v2 -[https://github.com/Azure/azureml-examples/tree/main/sdk/python/endpoints/online/kubernetes](https://github.com/Azure/azureml-examples/tree/main/sdk/python/endpoints/online/kubernetes)