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

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[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
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> [!WARNING]
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> 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).
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> 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).
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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.
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* [Publish and track machine learning pipelines](how-to-deploy-pipelines.md)
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* [Monitor Azure Machine Learning](../monitor-azure-machine-learning.md)
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* [Use automated ML in an Azure Machine Learning pipeline in Python](how-to-use-automlstep-in-pipelines.md)
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* [Use automated ML in an Azure Machine Learning pipeline in Python](how-to-use-automlstep-in-pipelines.md)

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