Merge pull request #232986 from MicrosoftDocs/release-azureml-monikers

Release azureml monikers--scheduled release ASAP

Author: huypub

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

@@ -1,8 +1,48 @@
 {
     "redirections": [
+    {
+        "source_path_from_root": "/articles/machine-learning/v1/how-to-network-security-overview.md",
+        "redirect_url": "/azure/machine-learning/how-to-network-security-overview",
+        "redirect_document_id": false
+    },
+    {
+        "source_path_from_root": "/articles/machine-learning/v1/how-to-workspace-diagnostic-api.md",
+        "redirect_url": "/azure/machine-learning/how-to-workspace-diagnostic-api",
+        "redirect_document_id": false
+    },
+    {
+        "source_path_from_root": "/articles/machine-learning/how-to-configure-cross-validation-data-splits.md",
+        "redirect_url": "/azure/machine-learning/v1/how-to-configure-cross-validation-data-splits",
+        "redirect_document_id": true
+    },
+    {
+        "source_path_from_root": "/articles/machine-learning/how-to-troubleshoot-auto-ml.md",
+        "redirect_url": "/azure/machine-learning/v1/how-to-troubleshoot-auto-ml",
+        "redirect_document_id": true
+    },
+    {
+        "source_path_from_root": "/articles/machine-learning/v1/how-to-export-delete-data.md",
+        "redirect_url": "/azure/machine-learning/how-to-export-delete-data.md",
+        "redirect_document_id": false   
+    },
+    {
+        "source_path_from_root": "/articles/machine-learning/how-to-configure-auto-features.md",
+        "redirect_url": "/azure/machine-learning/v1/how-to-configure-auto-features",
+        "redirect_document_id": true
+    },
+    {
+        "source_path_from_root": "/articles/machine-learning/how-to-generate-automl-training-code.md",
+        "redirect_url": "/azure/machine-learning/v1/how-to-generate-automl-training-code",
+        "redirect_document_id": true
+    },
+    {
+        "source_path_from_root": "/articles/machine-learning/how-to-configure-databricks-automl-environment.md",
+        "redirect_url": "/azure/machine-learning/v1/how-to-configure-databricks-automl-environment",
+        "redirect_document_id": true
+    },
     {
         "source_path_from_root": "/articles/machine-learning/reference-checkpoint-performance-with-Nebula.md",
-        "redirect_url": "/articles/machine-learning/reference-checkpoint-performance-for-large-models",
+        "redirect_url": "/azure/machine-learning/reference-checkpoint-performance-for-large-models",
         "redirect_document_id": true
     },
     {

articles/machine-learning/azure-machine-learning-glossary.md

@@ -9,6 +9,7 @@ author: frogglew
 ms.author: saoh
 ms.reviewer: sgilley
 ms.date: 09/21/2022
+monikerRange: 'azureml-api-2'
 ---
 
 # Azure Machine Learning glossary

articles/machine-learning/component-reference-v2/classification.md

@@ -34,7 +34,7 @@ AutoML creates a number of pipelines in parallel that try different algorithms a
 
 1. For **classification**, you can also enable deep learning.
 
-If deep learning is enabled, validation is limited to _train_validation split_. [Learn more about validation options](../how-to-configure-cross-validation-data-splits.md).
+If deep learning is enabled, validation is limited to _train_validation split_. [Learn more about validation options](../v1/how-to-configure-cross-validation-data-splits.md).
 
 
 1. (Optional) View addition configuration settings: additional settings you can use to better control the training job. Otherwise, defaults are applied based on experiment selection and data. 
@@ -45,12 +45,12 @@ If deep learning is enabled, validation is limited to _train_validation split_.
     Explain best model | Select to enable or disable, in order to show explanations for the recommended best model. <br> This functionality is not currently available for [certain forecasting algorithms](../v1/how-to-machine-learning-interpretability-automl.md#interpretability-during-training-for-the-best-model). 
     Blocked algorithm| Select algorithms you want to exclude from the training job. <br><br> Allowing algorithms is only available for [SDK experiments](../how-to-configure-auto-train.md#supported-algorithms). <br> See the [supported algorithms for each task type](/python/api/azureml-automl-core/azureml.automl.core.shared.constants.supportedmodels).
     Exit criterion| When any of these criteria are met, the training job is stopped. <br> *Training job time (hours)*: How long to allow the training job to run. <br> *Metric score threshold*:  Minimum metric score for all pipelines. This ensures that if you have a defined target metric you want to reach, you do not spend more time on the training job than necessary.
-    Concurrency| *Max concurrent iterations*: Maximum number of pipelines (iterations) to test in the training job. The job will not run more than the specified number of iterations. Learn more about how automated ML performs [multiple child jobs on clusters](/how-to-configure-auto-train.md#multiple-child-runs-on-clusters).
+    Concurrency| *Max concurrent iterations*: Maximum number of pipelines (iterations) to test in the training job. The job will not run more than the specified number of iterations. Learn more about how automated ML performs [multiple child jobs on clusters](../how-to-configure-auto-train.md#multiple-child-runs-on-clusters).
 
 
 1. The **[Optional] Validate and test** form allows you to do the following. 
 
-    1. Specify the type of validation to be used for your training job. [Learn more about cross validation](../how-to-configure-cross-validation-data-splits.md#prerequisites). 
+    1. Specify the type of validation to be used for your training job. [Learn more about cross validation](../v1/how-to-configure-cross-validation-data-splits.md#prerequisites). 
 
     1. Provide a test dataset (preview) to evaluate the recommended model that automated ML generates for you at the end of your experiment. When you provide test data, a test job is automatically triggered at the end of your experiment. This test job is only job on the best model that was recommended by automated ML. 
 

articles/machine-learning/component-reference-v2/regression.md

@@ -46,7 +46,7 @@ AutoML creates a number of pipelines in parallel that try different algorithms a
 
 1. The **[Optional] Validate and test** form allows you to do the following. 
 
-    1. Specify the type of validation to be used for your training job. [Learn more about cross validation](../how-to-configure-cross-validation-data-splits.md#prerequisites). 
+    1. Specify the type of validation to be used for your training job. [Learn more about cross validation](../v1/how-to-configure-cross-validation-data-splits.md#prerequisites). 
 
 
     1. Provide a test dataset (preview) to evaluate the recommended model that automated ML generates for you at the end of your experiment. When you provide test data, a test job is automatically triggered at the end of your experiment. This test job is only job on the best model that was recommended by automated ML.

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

@@ -17,7 +17,7 @@ ms.custom: automl, event-tier1-build-2022, ignite-2022
 
 [!INCLUDE [sdk v2](../../includes/machine-learning-sdk-v2.md)]
 > [!div class="op_single_selector" title1="Select the version of the Azure Machine Learning Python SDK you are using:"]
-> * [v1](./v1/concept-automated-ml-v1.md)
+> * [v1](./v1/concept-automated-ml-v1.md?view=azureml-api-1&preserve-view=true)
 > * [v2 (current version)](concept-automated-ml.md)
 
 Automated machine learning, also referred to as automated ML or AutoML, is the process of automating the time-consuming, iterative tasks of machine learning model development. It allows data scientists, analysts, and developers to build ML models with high scale, efficiency, and productivity all while sustaining model quality. Automated ML in Azure Machine Learning is based on a breakthrough from our [Microsoft Research division](https://www.microsoft.com/research/project/automl/).
@@ -48,7 +48,7 @@ The following diagram illustrates this process.
 
 You can also inspect the logged job information, which [contains metrics](how-to-understand-automated-ml.md) gathered during the job. The training job produces a Python serialized object (`.pkl` file) that contains the model and data preprocessing.
 
-While model building is automated, you can also [learn how important or relevant features are](./v1/how-to-configure-auto-train-v1.md#explain) to the generated models.
+While model building is automated, you can also [learn how important or relevant features are](./v1/how-to-configure-auto-train-v1.md?view=azureml-api-1&preserve-view=true#explain) to the generated models.
 
 ## When to use AutoML: classification, regression, forecasting, computer vision & NLP
 
@@ -154,7 +154,7 @@ Learn how to [configure AutoML experiments to use test data (preview) with the S
 
 Feature engineering is the process of using domain knowledge of the data to create features that help ML algorithms learn better. In Azure Machine Learning, scaling and normalization techniques are applied to facilitate feature engineering. Collectively, these techniques and feature engineering are referred to as featurization.
 
-For automated machine learning experiments, featurization is applied automatically, but can also be customized based on your data. [Learn more about what featurization is included](how-to-configure-auto-features.md#featurization) and how AutoML helps [prevent over-fitting and imbalanced data](concept-manage-ml-pitfalls.md) in your models.  
+For automated machine learning experiments, featurization is applied automatically, but can also be customized based on your data. [Learn more about what featurization is included (SDK v1)](./v1/how-to-configure-auto-features.md?view=azureml-api-1&preserve-view=true#featurization) and how AutoML helps [prevent over-fitting and imbalanced data](concept-manage-ml-pitfalls.md) in your models.  
 
 > [!NOTE]
 > Automated machine learning featurization steps (feature normalization, handling missing data,
@@ -212,7 +212,7 @@ How-to articles provide additional detail into what functionality automated ML o
 
 +  Learn how to [train computer vision models with Python](how-to-auto-train-image-models.md).
 
-+  Learn how to [view the generated code from your automated ML models](how-to-generate-automl-training-code.md).
++  Learn how to [view the generated code from your automated ML models (SDK v1)](./v1/how-to-generate-automl-training-code.md?view=azureml-api-1&preserve-view=true).
 
 ### Jupyter notebook samples 
 

articles/machine-learning/concept-automl-forecasting-sweeping.md

@@ -53,7 +53,7 @@ AutoML follows the usual cross-validation procedure, training a separate model o
 
 Cross-validation for forecasting jobs is configured by setting the number of cross-validation folds and, optionally, the number of time periods between two consecutive cross-validation folds. See the [custom cross-validation settings](./how-to-auto-train-forecast.md#custom-cross-validation-settings) guide for more information and an example of configuring cross-validation for forecasting.
 
-You can also bring your own validation data. Learn more in the [configure data splits and cross-validation in AutoML](how-to-configure-cross-validation-data-splits.md#provide-validation-data) article.
+You can also bring your own validation data. Learn more in the [configure data splits and cross-validation in AutoML (SDK v1)](./v1/how-to-configure-cross-validation-data-splits.md#provide-validation-data) article.
 
 ## Next steps
 * Learn more about [how to set up AutoML to train a time-series forecasting model](./how-to-auto-train-forecast.md).

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

@@ -18,7 +18,7 @@ ms.date: 11/04/2022
 
 [!INCLUDE [dev v2](../../includes/machine-learning-dev-v2.md)]
 
-This article applies to the second version of the [Azure Machine Learning CLI & Python SDK (v2)](concept-v2.md). For version one (v1), see [How Azure Machine Learning works: Architecture and concepts (v1)](v1/concept-azure-machine-learning-architecture.md)
+This article applies to the second version of the [Azure Machine Learning CLI & Python SDK (v2)](concept-v2.md). For version one (v1), see [How Azure Machine Learning works: Architecture and concepts (v1)](v1/concept-azure-machine-learning-architecture.md?view=azureml-api-1&preserve-view=true)
 
 Azure Machine Learning includes several resources and assets to enable you to perform your machine learning tasks. These resources and assets are needed to run any job.
 

articles/machine-learning/concept-compute-instance.md

@@ -11,6 +11,7 @@ ms.author: jcioffi
 author: jesscioffi
 ms.reviewer: sgilley
 ms.date: 10/19/2022
+monikerRange: 'azureml-api-2 || azureml-api-1'
 #Customer intent: As a data scientist, I want to know what a compute instance is and how to use it for Azure Machine Learning.
 ---
 
@@ -90,7 +91,7 @@ Python packages are all installed in the **Python 3.8 - AzureML** environment. C
 
 ## Accessing files
 
-Notebooks and Python scripts are stored in the default storage account of your workspace in Azure file share.  These files are located under your “User files” directory. This storage makes it easy to share notebooks between compute instances. The storage account also keeps your notebooks safely preserved when you stop or delete a compute instance.
+Notebooks and Python scripts are stored in the default storage account of your workspace in Azure file share.  These files are located under your "User files" directory. This storage makes it easy to share notebooks between compute instances. The storage account also keeps your notebooks safely preserved when you stop or delete a compute instance.
 
 The Azure file share account of your workspace is mounted as a drive on the compute instance. This drive is the default working directory for Jupyter, Jupyter Labs, RStudio, and Posit Workbench. This means that the notebooks and other files you create in Jupyter, JupyterLab, RStudio, or Posit are automatically stored on the file share and available to use in other compute instances as well.
 
@@ -100,8 +101,11 @@ You can also clone the latest Azure Machine Learning samples to your folder unde
 
 Writing small files can be slower on network drives than writing to the compute instance local disk itself.  If you're writing many small files, try using a directory directly on the compute instance, such as a `/tmp` directory. Note these files won't be accessible from other compute instances.
 
-Don't store training data on the notebooks file share. You can use the `/tmp` directory on the compute instance for your temporary data.  However, don't write large files of data on the OS disk of the compute instance. OS disk on compute instance has 128-GB capacity. You can also store temporary training data on temporary disk mounted on /mnt. Temporary disk size is based on the VM size chosen and can store larger amounts of data if a higher size VM is chosen. You can also mount [datastores and datasets](v1/concept-azure-machine-learning-architecture.md#datasets-and-datastores). Any software packages you install are saved on the OS disk of compute instance. Note customer managed key encryption is currently not supported for OS disk. The OS disk for compute instance is encrypted with Microsoft-managed keys. 
+Don't store training data on the notebooks file share. You can use the `/tmp` directory on the compute instance for your temporary data.  However, don't write large files of data on the OS disk of the compute instance. OS disk on compute instance has 128-GB capacity. You can also store temporary training data on temporary disk mounted on /mnt. Temporary disk size is based on the VM size chosen and can store larger amounts of data if a higher size VM is chosen. Any software packages you install are saved on the OS disk of compute instance. Note customer managed key encryption is currently not supported for OS disk. The OS disk for compute instance is encrypted with Microsoft-managed keys. 
 
+:::moniker range="azureml-api-1"
+You can also mount [datastores and datasets](v1/concept-azure-machine-learning-architecture.md?view=azureml-api-1&preserve-view=true#datasets-and-datastores). 
+:::moniker-end
 ## Create
 
 Follow the steps in the [Quickstart: Create workspace resources you need to get started with Azure Machine Learning](quickstart-create-resources.md) to create a basic compute instance.  

articles/machine-learning/concept-compute-target.md

@@ -11,6 +11,7 @@ author: vijetajo
 ms.reviewer: sgilley
 ms.date: 10/19/2022
 ms.custom: ignite-fall-2021, event-tier1-build-2022, cliv2
+monikerRange: 'azureml-api-2 || azureml-api-1'
 #Customer intent: As a data scientist, I want to understand what a compute target is and why I need it.
 ---
 
@@ -39,7 +40,12 @@ When performing inference, Azure Machine Learning creates a Docker container tha
 
 [!INCLUDE [aml-deploy-target](../../includes/aml-compute-target-deploy.md)]
 
+:::moniker range="azureml-api-2"
 Learn [where and how to deploy your model to a compute target](how-to-deploy-online-endpoints.md).
+:::moniker-end
+:::moniker range="azureml-api-1"
+Learn [where and how to deploy your model to a compute target](./v1/how-to-deploy-and-where.md).
+:::moniker-end
 
 ## Azure Machine Learning compute (managed)
 
@@ -118,7 +124,9 @@ While Azure Machine Learning supports these VM series, they might not be availab
 > [!NOTE]
 > Azure Machine Learning doesn't support all VM sizes that Azure Compute supports. To list the available VM sizes, use one of the following methods:
 > * [REST API](https://github.com/Azure/azure-rest-api-specs/blob/master/specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2020-08-01/examples/ListVMSizesResult.json)
+:::moniker range="azureml-api-2"
 > * The [Azure CLI extension 2.0 for machine learning](how-to-configure-cli.md) command, [az ml compute list-sizes](/cli/azure/ml/compute#az-ml-compute-list-sizes).
+:::moniker-end
 
 If using the GPU-enabled compute targets, it is important to ensure that the correct CUDA drivers are installed in the training environment. Use the following table to determine the correct CUDA version to use:
 
@@ -161,15 +169,27 @@ Azure Machine Learning supports the following unmanaged compute types:
 * Azure HDInsight
 * Azure Databricks
 * Azure Data Lake Analytics
+:::moniker range="azureml-api-1"
 * [Azure Synapse Spark pool](v1/how-to-link-synapse-ml-workspaces.md) (preview)
 
     > [!TIP]
     > Currently this requires the Azure Machine Learning SDK v1.
+:::moniker-end
+:::moniker range="azureml-api-2"
 * [Kubernetes](how-to-attach-kubernetes-anywhere.md)
+:::moniker-end
+:::moniker range="azureml-api-1"
+* [Azure Kubernetes Service](./v1/how-to-create-attach-kubernetes.md)
+:::moniker-end
 
 For more information, see [Manage compute resources](how-to-create-attach-compute-studio.md).
 
 ## Next steps
 
 Learn how to:
+:::moniker range="azureml-api-2"
 * [Deploy your model to a compute target](how-to-deploy-online-endpoints.md)
+:::moniker-end
+:::moniker range="azureml-api-1"
+* [Deploy your model](./v1/how-to-deploy-and-where.md)
+:::moniker-end