Merge pull request #5358 from MicrosoftDocs/main

Merge main to live, 4 AM

Author: v-ccolin

articles/machine-learning/v1/how-to-debug-parallel-run-step.md

@@ -10,7 +10,7 @@ ms.custom: UpdateFrequency5, troubleshooting, sdkv1, devx-track-python
 ms.reviewer: None
 ms.author: lagayhar
 author: lgayhardt
-ms.date: 11/16/2022
+ms.date: 06/03/2025
 #Customer intent: As a data scientist, I want to figure out why my ParallelRunStep doesn't run so that I can fix it.
 ---
 

articles/machine-learning/v1/how-to-use-synapsesparkstep.md

@@ -8,7 +8,7 @@ ms.subservice: mldata
 ms.author: zhanxia
 author: xiaoharper
 ms.reviewer: franksolomon
-ms.date: 03/07/2025
+ms.date: 06/03/2025
 ms.topic: how-to
 ms.custom: UpdateFrequency5, synapse-azureml, sdkv1
 #Customer intent: As a user of both Azure Machine Learning pipelines and Azure Synapse Analytics, I'd like to use Apache Spark for the data preparation of my pipeline
@@ -21,7 +21,7 @@ ms.custom: UpdateFrequency5, synapse-azureml, sdkv1
 [!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
 
 > [!WARNING]
-> The Azure Synapse Analytics integration with Azure Machine Learning, available in Python SDK v1, is deprecated. Users can still use Synapse workspace, registered with Azure Machine Learning, as a linked service. However, a new Synapse workspace can no longer be registered with Azure Machine Learning as a linked service. We recommend use of serverless Spark compute and attached Synapse Spark pools, available in CLI v2 and Python SDK v2. For more information, visit [https://aka.ms/aml-spark](https://aka.ms/aml-spark).
+> The Azure Synapse Analytics integration with Azure Machine Learning, available in Python SDK v1, is deprecated. Users can still use Synapse workspace, registered with Azure Machine Learning, as a linked service. However, a new Synapse workspace can no longer be registered with Azure Machine Learning as a linked service. We recommend use of serverless Spark compute and attached Synapse Spark pools, available in CLI v2 and Python SDK v2. For more information, see [Configure Apache Spark jobs in Azure Machine Learning](../quickstart-spark-jobs.md).
 
 In this article you learn how to use Apache Spark pools, powered by Azure Synapse Analytics, as the compute target for a data preparation step in an Azure Machine Learning pipeline. You learn how a single pipeline can use compute resources suited for the specific step - for example, data preparation or training. You'll also learn how data is prepared for the Spark step and how it passes to the next step.
 
@@ -246,4 +246,4 @@ The `pipeline.submit` call creates, if necessary, an Experiment named `synapse-p
 
 * [Publish and track machine learning pipelines](how-to-deploy-pipelines.md)
 * [Monitor Azure Machine Learning](../monitor-azure-machine-learning.md)
-* [Use automated ML in an Azure Machine Learning pipeline in Python](how-to-use-automlstep-in-pipelines.md)
+* [Use automated ML in an Azure Machine Learning pipeline in Python](how-to-use-automlstep-in-pipelines.md)