Merge pull request #248735 from sdgilley/sdg-update-v1-links

[BULK PR] update links to new branch

Author: v-dirichards

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

@@ -186,7 +186,7 @@ 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/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).
+See how to convert to ONNX format [in this Jupyter notebook example](https://github.com/Azure/azureml-examples/tree/v1-archive/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-automl-forecasting-methods.md

@@ -165,7 +165,7 @@ Each Series in Own Group (1:1) | All Series in Single Group (N:1)
 -------------------| -----------------
 Naive, Seasonal Naive, Average, Seasonal Average, Exponential Smoothing, ARIMA, ARIMAX, Prophet | Linear SGD, LARS LASSO, Elastic Net, K Nearest Neighbors, Decision Tree, Random Forest, Extremely Randomized Trees, Gradient Boosted Trees, LightGBM, XGBoost, TCNForecaster
 
-More general model groupings are possible via AutoML's Many-Models solution; see our [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) and [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).
+More general model groupings are possible via AutoML's Many-Models solution; see our [Many Models- Automated ML notebook](https://github.com/Azure/azureml-examples/blob/v1-archive/v1/python-sdk/tutorials/automl-with-azureml/forecasting-many-models/auto-ml-forecasting-many-models.ipynb) and [Hierarchical time series- Automated ML notebook](https://github.com/Azure/azureml-examples/blob/v1-archive/v1/python-sdk/tutorials/automl-with-azureml/forecasting-hierarchical-timeseries/auto-ml-forecasting-hierarchical-timeseries.ipynb).
 
 ## Next steps
 

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

@@ -58,7 +58,7 @@ 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 an Azure Machine Learning compute instance](how-to-create-compute-instance.md) for more information. 
 
-    * [Install the `automl` package yourself](https://github.com/Azure/azureml-examples/blob/main/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.
+    * [Install the `automl` package yourself](https://github.com/Azure/azureml-examples/blob/v1-archive/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-automl-forecasting-faq.md

@@ -25,9 +25,9 @@ You can start by reading the [Set up AutoML to train a time-series forecasting m
 
 - [Bike share example](https://github.com/Azure/azureml-examples/blob/main/sdk/python/jobs/automl-standalone-jobs/automl-forecasting-task-bike-share/auto-ml-forecasting-bike-share.ipynb)
 - [Forecasting using deep learning](https://github.com/Azure/azureml-examples/blob/main/sdk/python/jobs/automl-standalone-jobs/automl-forecasting-github-dau/auto-ml-forecasting-github-dau.ipynb)
-- [Many Models solution](https://github.com/Azure/azureml-examples/blob/main/v1/python-sdk/tutorials/automl-with-azureml/forecasting-many-models/auto-ml-forecasting-many-models.ipynb)
-- [Forecasting recipes](https://github.com/Azure/azureml-examples/blob/main/v1/python-sdk/tutorials/automl-with-azureml/forecasting-recipes-univariate/auto-ml-forecasting-univariate-recipe-experiment-settings.ipynb)
-- [Advanced forecasting scenarios](https://github.com/Azure/azureml-examples/blob/main/v1/python-sdk/tutorials/automl-with-azureml/forecasting-forecast-function/auto-ml-forecasting-function.ipynb)
+- [Many Models solution](https://github.com/Azure/azureml-examples/blob/v1-archive/v1/python-sdk/tutorials/automl-with-azureml/forecasting-many-models/auto-ml-forecasting-many-models.ipynb)
+- [Forecasting recipes](https://github.com/Azure/azureml-examples/blob/v1-archive/v1/python-sdk/tutorials/automl-with-azureml/forecasting-recipes-univariate/auto-ml-forecasting-univariate-recipe-experiment-settings.ipynb)
+- [Advanced forecasting scenarios](https://github.com/Azure/azureml-examples/blob/v1-archive/v1/python-sdk/tutorials/automl-with-azureml/forecasting-forecast-function/auto-ml-forecasting-function.ipynb)
 
 ## Why is AutoML slow on my data?
 
@@ -108,8 +108,8 @@ To choose between them, note that NRMSE penalizes outliers in the training data
 ## How can I improve the accuracy of my model?
 
 - Ensure that you're configuring AutoML the best way for your data. For more information, see the [What modeling configuration should I use?](#what-modeling-configuration-should-i-use) answer.
-- Check out the [forecasting recipes notebook](https://github.com/Azure/azureml-examples/blob/main/v1/python-sdk/tutorials/automl-with-azureml/forecasting-recipes-univariate/auto-ml-forecasting-univariate-recipe-experiment-settings.ipynb) for step-by-step guides on how to build and improve forecast models.  
-- Evaluate the model by using back tests over several forecasting cycles. This procedure gives a more robust estimate of forecasting error and gives you a baseline to measure improvements against. For an example, see the [back-testing notebook](https://github.com/Azure/azureml-examples/blob/main/v1/python-sdk/tutorials/automl-with-azureml/forecasting-backtest-single-model/auto-ml-forecasting-backtest-single-model.ipynb).
+- Check out the [forecasting recipes notebook](https://github.com/Azure/azureml-examples/blob/v1-archive/v1/python-sdk/tutorials/automl-with-azureml/forecasting-recipes-univariate/auto-ml-forecasting-univariate-recipe-experiment-settings.ipynb) for step-by-step guides on how to build and improve forecast models.  
+- Evaluate the model by using back tests over several forecasting cycles. This procedure gives a more robust estimate of forecasting error and gives you a baseline to measure improvements against. For an example, see the [back-testing notebook](https://github.com/Azure/azureml-examples/blob/v1-archive/v1/python-sdk/tutorials/automl-with-azureml/forecasting-backtest-single-model/auto-ml-forecasting-backtest-single-model.ipynb).
 - If the data is noisy, consider aggregating it to a coarser frequency to increase the signal-to-noise ratio. For more information, see [Frequency and target data aggregation](./how-to-auto-train-forecast.md#frequency--target-data-aggregation).
 - Add new features that can help predict the target. Subject matter expertise can help greatly when you're selecting training data.
 - Compare validation and test metric values, and determine if the selected model is underfitting or overfitting the data. This knowledge can guide you to a better training configuration. For example, you might determine that you need to use more cross-validation folds in response to overfitting.
@@ -140,7 +140,7 @@ AutoML supports the following advanced prediction scenarios:
 - Forecasting beyond the forecast horizon
 - Forecasting when there's a gap in time between training and forecasting periods
 
-For examples and details, see the [notebook for advanced forecasting scenarios](https://github.com/Azure/azureml-examples/blob/main/v1/python-sdk/tutorials/automl-with-azureml/forecasting-forecast-function/auto-ml-forecasting-function.ipynb).
+For examples and details, see the [notebook for advanced forecasting scenarios](https://github.com/Azure/azureml-examples/blob/v1-archive/v1/python-sdk/tutorials/automl-with-azureml/forecasting-forecast-function/auto-ml-forecasting-function.ipynb).
 
 ## How do I view metrics from forecasting training jobs?
 

articles/machine-learning/how-to-prepare-datasets-for-automl-images.md

@@ -25,7 +25,7 @@ In this article, you learn how to prepare image data for training computer visio
 To generate models for computer vision tasks with automated machine learning, you need to bring labeled image data as input for model training in the form of an `MLTable`. 
 
 You can create an `MLTable` from labeled training data in JSONL format. 
-If your labeled training data is in a different format (like, pascal VOC or COCO), you can use a [conversion script](https://github.com/Azure/azureml-examples/blob/main/v1/python-sdk/tutorials/automl-with-azureml/image-object-detection/coco2jsonl.py) to first convert it to JSONL, and then create an `MLTable`. Alternatively, you can use  Azure Machine Learning's [data labeling tool](how-to-create-image-labeling-projects.md) to manually label images, and export the labeled data to use for training your AutoML model.
+If your labeled training data is in a different format (like, pascal VOC or COCO), you can use a [conversion script](https://github.com/Azure/azureml-examples/blob/v1-archive/v1/python-sdk/tutorials/automl-with-azureml/image-object-detection/coco2jsonl.py) to first convert it to JSONL, and then create an `MLTable`. Alternatively, you can use  Azure Machine Learning's [data labeling tool](how-to-create-image-labeling-projects.md) to manually label images, and export the labeled data to use for training your AutoML model.
 
 ## Prerequisites
 
@@ -150,7 +150,7 @@ my_data = Data(
 
 Next, you will need to get the label annotations in JSONL format. The schema of labeled data depends on the computer vision task at hand. Refer to [schemas for JSONL files for AutoML computer vision experiments](reference-automl-images-schema.md) to learn more about the required JSONL schema for each task type.
 
-If your training data is in a different format (like, pascal VOC or COCO), [helper scripts](https://github.com/Azure/azureml-examples/blob/main/v1/python-sdk/tutorials/automl-with-azureml/image-object-detection/coco2jsonl.py) to convert the data to JSONL are available in [notebook examples](https://github.com/Azure/azureml-examples/blob/main/sdk/python/jobs/automl-standalone-jobs).
+If your training data is in a different format (like, pascal VOC or COCO), [helper scripts](https://github.com/Azure/azureml-examples/blob/v1-archive/v1/python-sdk/tutorials/automl-with-azureml/image-object-detection/coco2jsonl.py) to convert the data to JSONL are available in [notebook examples](https://github.com/Azure/azureml-examples/blob/main/sdk/python/jobs/automl-standalone-jobs).
 
 Once you have created jsonl file following the above steps, you can register it as a data asset using UI. Make sure you select `stream` type in schema section as shown below.
 

articles/machine-learning/how-to-use-automl-onnx-model-dotnet.md

@@ -25,7 +25,7 @@ ONNX is an open-source format for AI models. ONNX supports interoperability betw
 
 - [.NET Core SDK 3.1 or greater](https://dotnet.microsoft.com/download)
 - Text Editor or IDE (such as [Visual Studio](https://visualstudio.microsoft.com/vs/) or [Visual Studio Code](https://code.visualstudio.com/Download))
-- ONNX model. To learn how to train an AutoML ONNX model, see the following [bank marketing classification notebook (SDK v1)](https://github.com/Azure/azureml-examples/blob/main/v1/python-sdk/tutorials/automl-with-azureml/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb).
+- ONNX model. To learn how to train an AutoML ONNX model, see the following [bank marketing classification notebook (SDK v1)](https://github.com/Azure/azureml-examples/blob/v1-archive/v1/python-sdk/tutorials/automl-with-azureml/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb).
 - [Netron](https://github.com/lutzroeder/netron) (optional)
 
 ## Create a C# console application

articles/machine-learning/migrate-to-v2-execution-automl.md

@@ -24,7 +24,7 @@ This article gives a comparison of scenario(s) in SDK v1 and SDK v2.
 
 ## Submit AutoML run
 
-* SDK v1: Below is a sample AutoML classification task. For the entire code, check out our [examples repo](https://github.com/Azure/azureml-examples/blob/main/v1/python-sdk/tutorials/automl-with-azureml/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb).
+* SDK v1: Below is a sample AutoML classification task. For the entire code, check out our [examples repo](https://github.com/Azure/azureml-examples/blob/v1-archive/v1/python-sdk/tutorials/automl-with-azureml/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb).
 
     ```python
     # Imports

articles/machine-learning/migrate-to-v2-execution-pipeline.md

@@ -263,6 +263,6 @@ For more information, see the documentation here:
 
 * [steps in SDK v1](/python/api/azureml-pipeline-steps/azureml.pipeline.steps?view=azure-ml-py&preserve-view=true)
 * [Create and run machine learning pipelines using components with the Azure Machine Learning SDK v2](how-to-create-component-pipeline-python.md)
-* [Build a simple ML pipeline for image classification (SDK v1)](https://github.com/Azure/azureml-examples/blob/main/v1/python-sdk/tutorials/using-pipelines/image-classification.ipynb)
+* [Build a simple ML pipeline for image classification (SDK v1)](https://github.com/Azure/azureml-examples/blob/v1-archive/v1/python-sdk/tutorials/using-pipelines/image-classification.ipynb)
 * [OutputDatasetConfig](/python/api/azureml-core/azureml.data.output_dataset_config.outputdatasetconfig?view=azure-ml-py&preserve-view=true)
 * [`mldesigner`](https://pypi.org/project/mldesigner/)

articles/machine-learning/reference-automl-images-schema.md

@@ -429,7 +429,7 @@ In instance segmentation, output consists of multiple boxes with their scaled to
 > These settings are currently in public preview. They are provided without a service-level agreement. Certain features might not be supported or might have constrained capabilities. For more information, see [Supplemental Terms of Use for Microsoft Azure Previews](https://azure.microsoft.com/support/legal/preview-supplemental-terms/).
 
 > [!WARNING]
->  **Explainability** is supported only for **multi-class classification** and **multi-label classification**. While generating explanations on online endpoint, if you encounter timeout issues, use [batch scoring notebook (SDK v1)](https://github.com/Azure/azureml-examples/tree/main/v1/python-sdk/tutorials/automl-with-azureml/image-classification-multiclass-batch-scoring) to generate explanations.
+>  **Explainability** is supported only for **multi-class classification** and **multi-label classification**. While generating explanations on online endpoint, if you encounter timeout issues, use [batch scoring notebook (SDK v1)](https://github.com/Azure/azureml-examples/tree/v1-archive/v1/python-sdk/tutorials/automl-with-azureml/image-classification-multiclass-batch-scoring) to generate explanations.
 
 In this section, we document the input data format required to make predictions and generate explanations for the predicted class/classes using a deployed model. There's no separate deployment needed for explainability. The same endpoint for online scoring can be utilized to generate explanations. We just need to pass some extra explainability related parameters in input schema and get either visualizations of explanations and/or attribution score matrices (pixel level explanations).
 
@@ -574,7 +574,7 @@ If `model_explainability`, `visualizations`, `attributions` are set to `True` in
 
 
 > [!WARNING]
-> While generating explanations on online endpoint, make sure to select only few classes based on confidence score in order to avoid timeout issues on the endpoint or use the endpoint with GPU instance type. To generate explanations for large number of classes in multi-label classification, refer to [batch scoring notebook (SDK v1)](https://github.com/Azure/azureml-examples/tree/main/v1/python-sdk/tutorials/automl-with-azureml/image-classification-multiclass-batch-scoring).
+> While generating explanations on online endpoint, make sure to select only few classes based on confidence score in order to avoid timeout issues on the endpoint or use the endpoint with GPU instance type. To generate explanations for large number of classes in multi-label classification, refer to [batch scoring notebook (SDK v1)](https://github.com/Azure/azureml-examples/tree/v1-archive/v1/python-sdk/tutorials/automl-with-azureml/image-classification-multiclass-batch-scoring).
 
 ```json
 [

articles/machine-learning/tutorial-automated-ml-forecast.md

@@ -36,7 +36,7 @@ Also try automated machine learning for these other model types:
 
 * An Azure Machine Learning workspace. See [Create workspace resources](quickstart-create-resources.md). 
 
-* Download the [bike-no.csv](https://github.com/Azure/azureml-examples/blob/main/v1/python-sdk/tutorials/automl-with-azureml/forecasting-bike-share/bike-no.csv) data file
+* Download the [bike-no.csv](https://github.com/Azure/azureml-examples/blob/v1-archive/v1/python-sdk/tutorials/automl-with-azureml/forecasting-bike-share/bike-no.csv) data file
 
 ## Sign in to the studio
 
@@ -66,7 +66,7 @@ Before you configure your experiment, upload your data file to your workspace in
 
     1. Select **Upload files** from the **Upload** drop-down.. 
 
-    1. Choose the **bike-no.csv** file on your local computer. This is the file you downloaded as a [prerequisite](https://github.com/Azure/azureml-examples/blob/main/v1/python-sdk/tutorials/automl-with-azureml/forecasting-bike-share/bike-no.csv).
+    1. Choose the **bike-no.csv** file on your local computer. This is the file you downloaded as a [prerequisite](https://github.com/Azure/azureml-examples/blob/v1-archive/v1/python-sdk/tutorials/automl-with-azureml/forecasting-bike-share/bike-no.csv).
 
     1. Select **Next**