main/python-sdk/ -> ../v1/python-sdk

Author: sdgilley

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

@@ -90,15 +90,15 @@ Classification is a common machine learning task. Classification is a type of su
 
 The main goal of classification models is to predict which categories new data will fall into based on learnings from its training data. Common classification examples include fraud detection, handwriting recognition, and object detection. Learn more and see an example at [Create a classification model with automated ML](tutorial-first-experiment-automated-ml.md).
 
-See examples of classification and automated machine learning in these Python notebooks: [Fraud Detection](https://github.com/Azure/azureml-examples/blob/main/python-sdk/tutorials/automl-with-azureml/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb), [Marketing Prediction](https://github.com/Azure/azureml-examples/blob/main/python-sdk/tutorials/automl-with-azureml/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb), and [Newsgroup Data Classification](https://github.com/Azure/azureml-examples/tree/main/python-sdk/tutorials/automl-with-azureml/classification-text-dnn)
+See examples of classification and automated machine learning in these Python notebooks: [Fraud Detection](https://github.com/Azure/azureml-examples/blob/v2samplesreorg/v1/python-sdk/tutorials/automl-with-azureml/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb), [Marketing Prediction](https://github.com/Azure/azureml-examples/blob/v2samplesreorg/v1/python-sdk/tutorials/automl-with-azureml/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb), and [Newsgroup Data Classification](https://github.com/Azure/azureml-examples/tree/v2samplesreorg/v1/python-sdk/tutorials/automl-with-azureml/classification-text-dnn)
 
 ### Regression
 
 Similar to classification, regression tasks are also a common supervised learning task. Azure Machine Learning offers [featurizations specifically for these tasks](how-to-configure-auto-features.md#featurization).
 
 Different from classification where predicted output values are categorical, regression models predict numerical output values based on independent predictors. In regression, the objective is to help establish the relationship among those independent predictor variables by estimating how one variable impacts the others. For example, automobile price based on features like, gas mileage, safety rating, etc. Learn more and see an example of [regression with automated machine learning](v1/how-to-auto-train-models-v1.md).
 
-See examples of regression and automated machine learning for predictions in these Python notebooks: [CPU Performance Prediction](https://github.com/Azure/azureml-examples/tree/main/python-sdk/tutorials/automl-with-azureml/regression-explanation-featurization), 
+See examples of regression and automated machine learning for predictions in these Python notebooks: [CPU Performance Prediction](https://github.com/Azure/azureml-examples/tree/v2samplesreorg/v1/python-sdk/tutorials/automl-with-azureml/regression-explanation-featurization), 
 
 ### Time-series forecasting
 
@@ -115,7 +115,7 @@ Advanced forecasting configuration includes:
 * rolling window aggregate features
 
 
-See examples of regression and automated machine learning for predictions in these Python notebooks: [Sales Forecasting](https://github.com/Azure/azureml-examples/blob/main/python-sdk/tutorials/automl-with-azureml/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb), [Demand Forecasting](https://github.com/Azure/azureml-examples/blob/main/python-sdk/tutorials/automl-with-azureml/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb), and [Forecasting GitHub's Daily Active Users](https://github.com/Azure/azureml-examples/blob/main/python-sdk/tutorials/automl-with-azureml/forecasting-github-dau/auto-ml-forecasting-github-dau.ipynb).
+See examples of regression and automated machine learning for predictions in these Python notebooks: [Sales Forecasting](https://github.com/Azure/azureml-examples/blob/v2samplesreorg/v1/python-sdk/tutorials/automl-with-azureml/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb), [Demand Forecasting](https://github.com/Azure/azureml-examples/blob/v2samplesreorg/v1/python-sdk/tutorials/automl-with-azureml/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb), and [Forecasting GitHub's Daily Active Users](https://github.com/Azure/azureml-examples/blob/v2samplesreorg/v1/python-sdk/tutorials/automl-with-azureml/forecasting-github-dau/auto-ml-forecasting-github-dau.ipynb).
 
 ### Computer vision (preview)
 
@@ -295,7 +295,7 @@ See the [how-to](./v1/how-to-configure-auto-train-v1.md#ensemble) for changing d
 
 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/main/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/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).
 
 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). 
 
@@ -326,7 +326,7 @@ How-to articles provide additional detail into what functionality automated ML o
 
 ### Jupyter notebook samples 
 
-Review detailed code examples and use cases in the [GitHub notebook repository for automated machine learning samples](https://github.com/Azure/azureml-examples/tree/main/python-sdk/tutorials/automl-with-azureml).
+Review detailed code examples and use cases in the [GitHub notebook repository for automated machine learning samples](https://github.com/Azure/azureml-examples/tree/v2samplesreorg/v1/python-sdk/tutorials/automl-with-azureml).
 
 ### Python SDK reference
 

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/main/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/v2samplesreorg/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/main/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/v2samplesreorg/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/main/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/v2samplesreorg/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/main/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/v2samplesreorg/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/main/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/v2samplesreorg/v1/python-sdk/tutorials/automl-with-azureml/forecasting-hierarchical-timeseries/auto-ml-forecasting-hierarchical-timeseries.ipynb), for an end to end example. 
 
 ```python
 
@@ -428,12 +428,12 @@ hts_parameters = HTSTrainParameters(
 
 ## Example notebooks
 
-See the [forecasting sample notebooks](https://github.com/Azure/azureml-examples/tree/main/python-sdk/tutorials/automl-with-azureml) for detailed code examples of advanced forecasting configuration including:
+See the [forecasting sample notebooks](https://github.com/Azure/azureml-examples/tree/v2samplesreorg/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/main/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/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/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/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/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)
 
 
 ## Next steps

articles/machine-learning/how-to-auto-train-nlp-models.md

@@ -63,7 +63,7 @@ You can seamlessly integrate with the [Azure Machine Learning data labeling](how
     To install the SDK you can either, 
     * Create a compute instance, which automatically installs the SDK and is pre-configured for ML workflows. See [Create and manage an Azure Machine Learning compute instance](how-to-create-manage-compute-instance.md) for more information. 
 
-    * [Install the `automl` package yourself](https://github.com/Azure/azureml-examples/blob/main/python-sdk/tutorials/automl-with-azureml/README.md#setup-using-a-local-conda-environment), which includes the [default installation](/python/api/overview/azure/ml/install#default-install) of the SDK.
+    * [Install the `automl` package yourself](https://github.com/Azure/azureml-examples/blob/v2samplesreorg/v1/python-sdk/tutorials/automl-with-azureml/README.md#setup-using-a-local-conda-environment), which includes the [default installation](/python/api/overview/azure/ml/install#default-install) of the SDK.
 
     [!INCLUDE [automl-sdk-version](../../includes/machine-learning-automl-sdk-version.md)]
 

articles/machine-learning/how-to-configure-auto-features.md

@@ -344,7 +344,7 @@ In order to invoke BERT, set  `enable_dnn: True` in your automl_settings and use
 
 Automated ML takes the following steps for BERT. 
 
-1. **Preprocessing and tokenization of all text columns**. For example, the "StringCast" transformer can be found in the final model's featurization summary. An example of how to produce the model's featurization summary can be found in [this notebook](https://github.com/Azure/azureml-examples/blob/main/python-sdk/tutorials/automl-with-azureml/classification-text-dnn/auto-ml-classification-text-dnn.ipynb).
+1. **Preprocessing and tokenization of all text columns**. For example, the "StringCast" transformer can be found in the final model's featurization summary. An example of how to produce the model's featurization summary can be found in [this notebook](https://github.com/Azure/azureml-examples/blob/v2samplesreorg/v1/python-sdk/tutorials/automl-with-azureml/classification-text-dnn/auto-ml-classification-text-dnn.ipynb).
 
 2. **Concatenate all text columns into a single text column**, hence the `StringConcatTransformer` in the final model. 
 

articles/machine-learning/how-to-configure-databricks-automl-environment.md

@@ -108,7 +108,7 @@ In AutoML config, when using Azure Databricks add the following parameters:
 ## ML notebooks that work with Azure Databricks
 
 Try it out:
-+ While many sample notebooks are available, **only [these sample notebooks](https://github.com/Azure/azureml-examples/tree/main/python-sdk/tutorials/automl-with-databricks) work with Azure Databricks.**
++ While many sample notebooks are available, **only [these sample notebooks](https://github.com/Azure/azureml-examples/tree/v2samplesreorg/v1/python-sdk/tutorials/automl-with-databricks) work with Azure Databricks.**
 
 + Import these samples directly from your workspace. See below:
 ![Select Import](./media/how-to-configure-environment/azure-db-screenshot.png)

articles/machine-learning/how-to-generate-automl-training-code.md

@@ -51,7 +51,7 @@ The following diagram illustrates that you can generate the code for automated M
 
     * You can run your code via a Jupyter notebook in an [Azure Machine Learning compute instance](), which contains the latest Azure ML SDK already installed. The compute instance comes with a ready-to-use Conda environment that is compatible with the automated ML code generation (preview) capability.
 
-    * Alternatively, you can create a new local Conda environment on your local machine and then install the latest Azure ML SDK. [How to install AutoML client SDK in Conda environment with the `automl` package](https://github.com/Azure/azureml-examples/tree/main/python-sdk/tutorials/automl-with-azureml#setup-using-a-local-conda-environment).
+    * Alternatively, you can create a new local Conda environment on your local machine and then install the latest Azure ML SDK. [How to install AutoML client SDK in Conda environment with the `automl` package](https://github.com/Azure/azureml-examples/tree/v2samplesreorg/v1/python-sdk/tutorials/automl-with-azureml#setup-using-a-local-conda-environment).
 
 ##  Code generation with the SDK