Merge pull request #208310 from sdgilley/sdg-v2

Move tutorials to v1

Author: liljack17

articles/machine-learning/.openpublishing.redirection.machine-learning.json

@@ -1012,7 +1012,12 @@
     },
     {
         "source_path_from_root": "/articles/machine-learning/service/tutorial-auto-train-models.md",
-        "redirect_url": "/azure/machine-learning/tutorial-auto-train-models",
+        "redirect_url": "/azure/machine-learning/v1/how-to-auto-train-models-v1",
+        "redirect_document_id": false
+    },
+    {
+        "source_path_from_root": "/articles/machine-learning/tutorial-auto-train-models.md",
+        "redirect_url": "/azure/machine-learning/v1/how-to-auto-train-models-v1",
         "redirect_document_id": true
     },
     {

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

@@ -18,13 +18,12 @@ Automated machine learning, also referred to as automated ML or AutoML, is the p
 
 Traditional machine learning model development is resource-intensive, requiring significant domain knowledge and time to produce and compare dozens of models. With automated machine learning, you'll accelerate the time it takes to get production-ready ML models with great ease and efficiency.
 
-<a name="parity"></a>
 
 ## Ways to use AutoML in Azure Machine Learning
 
-Azure Machine Learning offers the following two experiences for working with automated ML. See the following sections to understand [feature availability in each experience](#parity).
+Azure Machine Learning offers the following two experiences for working with automated ML. See the following sections to understand feature availability in each experience.
 
-* For code-experienced customers, [Azure Machine Learning Python SDK](/python/api/overview/azure/ml/intro).  Get started with [Tutorial: Use automated machine learning to predict taxi fares](tutorial-auto-train-models.md).
+* For code-experienced customers, [Azure Machine Learning Python SDK](/python/api/overview/azure/ml/intro).  Get started with [Tutorial: Train an object detection model (preview) with AutoML and Python](tutorial-auto-train-image-models.md)
 
 * For limited/no-code experience customers, Azure Machine Learning studio at [https://ml.azure.com](https://ml.azure.com/).  Get started with these tutorials:
     * [Tutorial: Create a classification model with automated ML in Azure Machine Learning](tutorial-first-experiment-automated-ml.md).
@@ -97,7 +96,7 @@ See examples of classification and automated machine learning in these Python no
 
 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](tutorial-auto-train-models.md).
+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), 
 
@@ -176,7 +175,7 @@ Using **Azure Machine Learning**, you can design and run your automated ML train
 1. **Identify the ML problem** to be solved: classification, forecasting, regression or computer vision (preview).
 
 1. **Choose whether you want to use the Python SDK or the studio web experience**:
-   Learn about the parity between the [Python SDK and studio web experience](#parity).
+   Learn about the parity between the [Python SDK and studio web experience](#ways-to-use-automl-in-azure-machine-learning).
 
    * For limited or no code experience, try the Azure Machine Learning studio web experience at [https://ml.azure.com](https://ml.azure.com/)  
    * For Python developers, check out the [Azure Machine Learning Python SDK](how-to-configure-auto-train.md) 
@@ -308,7 +307,8 @@ There are multiple resources to get you up and running with AutoML.
 
 ### Tutorials/ how-tos
 Tutorials are end-to-end introductory examples of AutoML scenarios.
-+ **For a code first experience**, follow the [Tutorial: Train a regression model with AutoML and Python](tutorial-auto-train-models.md).
+
++ **For a code first experience**, follow the [Tutorial: Train an object detection model (preview) with AutoML and Python](tutorial-auto-train-image-models.md)
 
 + **For a low or no-code experience**, see the [Tutorial: Train a classification model with no-code AutoML in Azure Machine Learning studio](tutorial-first-experiment-automated-ml.md).
 

articles/machine-learning/concept-manage-ml-pitfalls.md

@@ -114,7 +114,7 @@ The following techniques are additional options to handle imbalanced data **outs
 
 See examples and learn how to build models using automated machine learning:
 
-+ Follow the [Tutorial: Automatically train a regression model with Azure Machine Learning](tutorial-auto-train-models.md)
++ Follow the [Tutorial: Train an object detection model (preview) with AutoML and Python](tutorial-auto-train-image-models.md).
 
 + Configure the settings for automatic training experiment:
   + In Azure Machine Learning studio, [use these steps](how-to-use-automated-ml-for-ml-models.md).

articles/machine-learning/concept-train-machine-learning-model.md

@@ -59,7 +59,6 @@ Define the iterations, hyperparameter settings, featurization, and other setting
 
 * [What is automated machine learning?](concept-automated-ml.md)
 * [Tutorial: Create your first classification model with automated machine learning](tutorial-first-experiment-automated-ml.md)
-* [Tutorial: Use automated machine learning to predict taxi fares](tutorial-auto-train-models.md)
 * [Examples: Jupyter Notebook examples for automated machine learning](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/automated-machine-learning)
 * [How to: Configure automated ML experiments in Python](how-to-configure-auto-train.md)
 * [How to: Autotrain a time-series forecast model](how-to-auto-train-forecast.md)

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

@@ -35,7 +35,7 @@ For this article you need,
 
 * An Azure Machine Learning workspace. To create the workspace, see [Create workspace resources](quickstart-create-resources.md).
 
-* This article assumes some familiarity with setting up an automated machine learning experiment. Follow the [tutorial](tutorial-auto-train-models.md) or [how-to](how-to-configure-auto-train.md) to see the main automated machine learning experiment design patterns.
+* This article assumes some familiarity with setting up an automated machine learning experiment. Follow the [how-to](how-to-configure-auto-train.md) to see the main automated machine learning experiment design patterns.
 
     [!INCLUDE [automl-sdk-version](../../includes/machine-learning-automl-sdk-version.md)]
 
@@ -429,6 +429,6 @@ See the [forecasting sample notebooks](https://github.com/Azure/azureml-examples
 
 ## Next steps
 
-* Learn more about [how and where to deploy a model](./v1/how-to-deploy-and-where.md).
+* Learn more about [How to deploy an AutoML model to an online endpoint](how-to-deploy-automl-endpoint.md).
 * Learn about [Interpretability: model explanations in automated machine learning (preview)](how-to-machine-learning-interpretability-automl.md). 
-* Follow the [Tutorial: Train regression models](tutorial-auto-train-models.md) for an end to end example for creating experiments with automated machine learning.
+

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

@@ -388,4 +388,4 @@ automl_settings = {
 
 * Learn more about [how and where to deploy a model](./v1/how-to-deploy-and-where.md).
 
-* Learn more about [how to train a regression model by using automated machine learning](tutorial-auto-train-models.md) or [how to train by using automated machine learning on a remote resource](./v1/concept-automated-ml-v1.md#local-remote).
+* Learn more about [how to train a regression model by using automated machine learning](./v1/how-to-auto-train-models-v1.md) or [how to train by using automated machine learning on a remote resource](./v1/concept-automated-ml-v1.md#local-remote).

articles/machine-learning/how-to-configure-cross-validation-data-splits.md

@@ -31,7 +31,7 @@ For this article you need,
 
 * An Azure Machine Learning workspace. To create the workspace, see [Create workspace resources](quickstart-create-resources.md).
 
-* Familiarity with setting up an automated machine learning experiment with the Azure Machine Learning SDK. Follow the [tutorial](tutorial-auto-train-models.md) or [how-to](how-to-configure-auto-train.md) to see the fundamental automated machine learning experiment design patterns.
+* Familiarity with setting up an automated machine learning experiment with the Azure Machine Learning SDK. Follow the [tutorial](tutorial-auto-train-image-models.md) or [how-to](how-to-configure-auto-train.md) to see the fundamental automated machine learning experiment design patterns.
 
 * An understanding of train/validation data splits and cross-validation as machine learning concepts. For a high-level explanation,
 
@@ -251,5 +251,5 @@ Passing the `test_data` or `test_size` parameters into the `AutoMLConfig`, autom
 ## Next steps
 
 * [Prevent imbalanced data and overfitting](concept-manage-ml-pitfalls.md).
-* [Tutorial: Use automated machine learning to predict taxi fares - Split data section](tutorial-auto-train-models.md#split-the-data-into-train-and-test-sets).
+
 * How to [Auto-train a time-series forecast model](how-to-auto-train-forecast.md).

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

@@ -43,7 +43,7 @@ The following diagram illustrates that you can generate the code for automated M
 
 * An Azure Machine Learning workspace. To create the workspace, see [Create workspace resources](quickstart-create-resources.md).
 
-* This article assumes some familiarity with setting up an automated machine learning experiment. Follow the [tutorial](tutorial-auto-train-models.md) or [how-to](how-to-configure-auto-train.md) to see the main automated machine learning experiment design patterns.
+* This article assumes some familiarity with setting up an automated machine learning experiment. Follow the [tutorial](tutorial-auto-train-image-models.md) or [how-to](how-to-configure-auto-train.md) to see the main automated machine learning experiment design patterns.
 
 * Automated ML code generation is only available for experiments run on remote Azure ML compute targets. Code generation isn't supported for local runs.
 
@@ -484,5 +484,5 @@ However, in order to load that model in a notebook in your custom local Conda en
 
 ## Next steps
 
-* Learn more about [how and where to deploy a model](/azure/machine-learning/how-to-deploy-managed-online-endpoints).
+* Learn more about [how and where to deploy a model](how-to-deploy-and-where.md).
 * See how to [enable interpretability features](how-to-machine-learning-interpretability-automl.md) specifically within automated ML experiments.

articles/machine-learning/how-to-machine-learning-interpretability-automl.md

@@ -30,7 +30,7 @@ In this article, you learn how to:
 ## Prerequisites
 
 - Interpretability features. Run `pip install azureml-interpret` to get the necessary package.
-- Knowledge of building automated ML experiments. For more information on how to use the Azure Machine Learning SDK, complete this [regression model tutorial](tutorial-auto-train-models.md) or see how to [configure automated ML experiments](how-to-configure-auto-train.md).
+- Knowledge of building automated ML experiments. For more information on how to use the Azure Machine Learning SDK, complete this [object detection model tutorial](tutorial-auto-train-image-models.md) or see how to [configure automated ML experiments](how-to-configure-auto-train.md).
 
 ## Interpretability during training for the best model
 

articles/machine-learning/how-to-troubleshoot-auto-ml.md

@@ -207,6 +207,6 @@ If you have over 100 automated ML experiments, this may cause new automated ML e
 
 ## Next steps
 
-+ Learn more about [how to train a regression model with Automated machine learning](tutorial-auto-train-models.md) or [how to train using Automated machine learning on a remote resource](./v1/concept-automated-ml-v1.md#local-remote).
++ Learn more about [how to train a regression model with Automated machine learning](./v1/how-to-auto-train-models-v1.md) or [how to train using Automated machine learning on a remote resource](./v1/concept-automated-ml-v1.md#local-remote).
 
 + Learn more about [how and where to deploy a model](./v1/how-to-deploy-and-where.md).