Merge pull request #241965 from Blackmist/v1-v2-checkup

misc. updates for v1/v2

Author: v-dirichards

articles/machine-learning/how-to-mlflow-batch.md

@@ -262,7 +262,7 @@ Azure Machine Learning supports no-code deployment for batch inference in [manag
 
 ### How work is distributed on workers
 
-Work is distributed at the file level, for both structured and unstructured data. As a consequence, only [file datasets](v1/how-to-create-register-datasets.md#filedataset) or [URI folders](reference-yaml-data.md) are supported for this feature. Each worker processes batches of `Mini batch size` files at a time. Further parallelism can be achieved if `Max concurrency per instance` is increased. 
+Work is distributed at the file level, for both structured and unstructured data. As a consequence, only [file datasets (v1 API)](v1/how-to-create-register-datasets.md#filedataset) or [URI folders](reference-yaml-data.md) are supported for this feature. Each worker processes batches of `Mini batch size` files at a time. Further parallelism can be achieved if `Max concurrency per instance` is increased. 
 
 > [!WARNING]
 > Nested folder structures are not explored during inference. If you are partitioning your data using folders, make sure to flatten the structure beforehand.

articles/machine-learning/how-to-private-endpoint-integration-synapse.md

@@ -17,9 +17,6 @@ ms.date: 11/16/2022
 
 In this article, learn how to securely integrate with Azure Machine Learning from Azure Synapse. This integration enables you to use Azure Machine Learning from notebooks in your Azure Synapse workspace. Communication between the two workspaces is secured using an Azure Virtual Network.
 
-> [!TIP]
-> You can also perform integration in the opposite direction, using Azure Synapse spark pool from Azure Machine Learning. For more information, see [Link Azure Synapse and Azure Machine Learning](v1/how-to-link-synapse-ml-workspaces.md).
-
 ## Prerequisites
 
 * An Azure subscription.

articles/machine-learning/how-to-troubleshoot-protobuf-descriptor-error.md

@@ -10,14 +10,15 @@ ms.author: larryfr
 ms.reviewer: larryfr
 ms.topic: troubleshooting 
 ms.date: 11/04/2022
+monikerRange: 'azureml-api-1 || azureml-api-2'
 ---
 
 # Troubleshoot `descriptors cannot not be created directly` error
 
 When using Azure Machine Learning, you may receive the following error:
 
 ```
-TypeError: Descriptors cannot not be created directly. If this call came from a _pb2.py file, your generated code is out of date and must be regenerated with protoc >= 3.19.0.” It is followed by the proposition to install the appropriate version of protobuf library.
+TypeError: Descriptors cannot not be created directly. If this call came from a _pb2.py file, your generated code is out of date and must be regenerated with protoc >= 3.19.0." It is followed by the proposition to install the appropriate version of protobuf library.
 
 If you cannot immediately regenerate your protos, some other possible workarounds are:
  1. Downgrade the protobuf package to 3.20.x or lower.
@@ -40,9 +41,14 @@ pip install azureml-sdk[automl,explain,notebooks]>=1.42.0
 
 For more information on updating an Azure Machine Learning environment (for training or deployment), see the following articles:
 
+:::moniker range="azureml-api-1"
 * [Manage environments in studio](how-to-manage-environments-in-studio.md#rebuild-an-environment)
-* [Create & use software environments (SDK v1)](how-to-use-environments.md#update-an-existing-environment)
-* [Create & manage environments (CLI v2)](how-to-manage-environments-v2.md#update)
+* [Create & use software environments](./v1/how-to-use-environments.md)
+:::moniker-end
+:::moniker range="azureml-api-2"
+* [Manage environments in studio](how-to-manage-environments-in-studio.md#rebuild-an-environment)
+* [Create & manage environments](how-to-manage-environments-v2.md#update)
+:::moniker-end
 
 To verify the version of your installed SDK, use the following command:
 
@@ -80,6 +86,11 @@ For more information on the breaking changes in protobuf 4.0.0, see [https://dev
 
 For more information on updating an Azure Machine Learning environment (for training or deployment), see the following articles:
 
+:::moniker range="azureml-api-1"
+* [Manage environments in studio](how-to-manage-environments-in-studio.md#rebuild-an-environment)
+* [Create & use software environments](./v1/how-to-use-environments.md)
+:::moniker-end
+:::moniker range="azureml-api-2"
 * [Manage environments in studio](how-to-manage-environments-in-studio.md#rebuild-an-environment)
-* [Create & use software environments (SDK v1)](how-to-use-environments.md#update-an-existing-environment)
-* [Create & manage environments (CLI v2)](how-to-manage-environments-v2.md#update)
+* [Create & manage environments](how-to-manage-environments-v2.md#update)
+:::moniker-end

articles/machine-learning/toc.yml

@@ -1294,7 +1294,7 @@
     - name: Troubleshoot secure workspace connectivity
       href: how-to-troubleshoot-secure-connection-workspace.md
     - name: Troubleshoot SerializationError
-      href: how-to-troubleshoot-serialization-error.md
+      href: ./v1/how-to-troubleshoot-serialization-error.md
     - name: Troubleshoot descriptor error
       displayName: descriptors cannot not be created directly
       href: how-to-troubleshoot-protobuf-descriptor-error.md

articles/machine-learning/how-to-troubleshoot-serialization-error.md renamed to articles/machine-learning/v1/how-to-troubleshoot-serialization-error.md

@@ -36,9 +36,8 @@ pip install azureml-sdk[automl,explain,notebooks]>=1.42.0
 
 For more information on updating an Azure Machine Learning environment (for training or deployment), see the following articles:
 
-* [Manage environments in studio](how-to-manage-environments-in-studio.md#rebuild-an-environment)
-* [Create & use software environments (SDK v1)](how-to-use-environments.md#update-an-existing-environment)
-* [Create & manage environments (CLI v2)](how-to-manage-environments-v2.md#update)
+* [Manage environments in studio](../how-to-manage-environments-in-studio.md#rebuild-an-environment)
+* [Create & use software environments](how-to-use-environments.md)
 
 To verify the version of your installed SDK, use the following command:
 
@@ -50,6 +49,5 @@ pip show azureml-core
 
 For more information on updating an Azure Machine Learning environment (for training or deployment), see the following articles:
 
-* [Manage environments in studio](how-to-manage-environments-in-studio.md#rebuild-an-environment)
-* [Create & use software environments (SDK v1)](how-to-use-environments.md#update-an-existing-environment)
-* [Create & manage environments (CLI v2)](how-to-manage-environments-v2.md#update)
+* [Manage environments in studio](../how-to-manage-environments-in-studio.md#rebuild-an-environment)
+* [Create & use software environments](how-to-use-environments.md)