Merge pull request #208778 from ssalgadodev/v1v2DataDropDown

Data | Added v1 v2 drop down

Author: prmerger-automator[bot]

articles/machine-learning/how-to-administrate-data-authentication.md

@@ -15,6 +15,11 @@ ms.date: 05/24/2022
 ---
 
 # Data administration
+
+> [!div class="op_single_selector" title1="Select the version of Azure Machine Learning SDK or CLI extension you are using:"]
+> * [v1](./v1/concept-network-data-access.md)
+> * [v2 (current version)](how-to-administrate-data-authentication.md)
+
 Learn how to manage data access and how to authenticate in Azure Machine Learning
 [!INCLUDE [sdk v2](../../includes/machine-learning-sdk-v2.md)]
 [!INCLUDE [CLI v2](../../includes/machine-learning-CLI-v2.md)]

articles/machine-learning/how-to-read-write-data-v2.md

@@ -16,6 +16,10 @@ ms.custom: devx-track-python, devplatv2, sdkv2, cliv2, event-tier1-build-2022
 
 # Read and write data in a job
 
+> [!div class="op_single_selector" title1="Select the version of Azure Machine Learning CLI extension you are using:"]
+> * [v1](v1/how-to-train-with-datasets.md)
+> * [v2 (current version)](how-to-read-write-data-v2.md)
+
 [!INCLUDE [sdk v2](../../includes/machine-learning-sdk-v2.md)]
 [!INCLUDE [CLI v2](../../includes/machine-learning-CLI-v2.md)]
 

articles/machine-learning/v1/concept-network-data-access.md

@@ -15,6 +15,13 @@ ms.date: 11/19/2021
 
 # Network data access with Azure Machine Learning studio
 
+> [!div class="op_single_selector" title1="Select the version of Azure Machine Learning developer platform you are using:"]
+> * [v1](concept-network-data-access.md)
+> * [v2 (current version)](../how-to-administrate-data-authentication.md)
+
+[!INCLUDE [sdk v1](../../../includes/machine-learning-sdk-v1.md)]
+[!INCLUDE [cli v1](../../../includes/machine-learning-cli-v1.md)]
+
 Data access is complex and it's important to recognize that there are many pieces to it. For example, accessing data from Azure Machine Learning studio is different than using the SDK. When using the SDK on your local development environment, you're directly accessing data in the cloud. When using studio, you aren't always directly accessing the data store from your client. Studio relies on the workspace to access data on your behalf.
 
 > [!IMPORTANT]

articles/machine-learning/v1/how-to-train-with-datasets.md

@@ -7,7 +7,7 @@ ms.service: machine-learning
 ms.subservice: mldata
 ms.author: yogipandey
 author: ynpandey
-ms.reviewer: nibaccam
+ms.reviewer: ssalgado
 ms.date: 10/21/2021
 ms.topic: how-to
 ms.custom: devx-track-python, data4ml, sdkv1, event-tier1-build-2022
@@ -16,6 +16,10 @@ ms.custom: devx-track-python, data4ml, sdkv1, event-tier1-build-2022
 
 # Train models with Azure Machine Learning datasets 
 
+> [!div class="op_single_selector" title1="Select the version of Azure Machine Learning CLI extension you are using:"]
+> * [v1](how-to-train-with-datasets.md)
+> * [v2 (current version)](../how-to-read-write-data-v2.md)
+
 [!INCLUDE [sdk v1](../../../includes/machine-learning-sdk-v1.md)]
 
 In this article, you learn how to work with [Azure Machine Learning datasets](/python/api/azureml-core/azureml.core.dataset%28class%29) to train machine learning models.  You can use datasets in your local or remote compute target without worrying about connection strings or data paths.