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Larry Franks
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removing note about endpoints being preview
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articles/machine-learning/apache-spark-azure-ml-concepts.md

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- Managed (Automatic) Spark compute
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- Attached Synapse Spark pool
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[!INCLUDE [machine-learning-preview-generic-disclaimer](../../includes/machine-learning-preview-generic-disclaimer.md)]
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## Managed (Automatic) Spark compute
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With the Apache Spark framework, Azure Machine Learning Managed (Automatic) Spark compute is the easiest way to accomplish distributed computing tasks in the Azure Machine Learning environment. Azure Machine Learning offers a fully managed, serverless, on-demand Apache Spark compute cluster. Its users can avoid the need to create an Azure Synapse workspace and a Synapse Spark pool.

articles/machine-learning/concept-designer.md

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To learn how to deploy your model, see [Tutorial: Deploy a machine learning model with the designer](tutorial-designer-automobile-price-deploy.md).
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[!INCLUDE [endpoints-option](../../includes/machine-learning-endpoints-preview-note.md)]
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## Publish
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You can also publish a pipeline to a **pipeline endpoint**. Similar to an online endpoint, a pipeline endpoint lets you submit new pipeline jobs from external applications using REST calls. However, you cannot send or receive data in real time using a pipeline endpoint.

articles/machine-learning/how-to-run-batch-predictions-designer.md

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To learn how to set up batch scoring services using the SDK, see the accompanying [tutorial on pipeline batch scoring](./tutorial-pipeline-batch-scoring-classification.md).
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[!INCLUDE [endpoints-option](../../includes/machine-learning-endpoints-preview-note.md)]
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## Prerequisites
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This how-to assumes you already have a training pipeline. For a guided introduction to the designer, complete [part one of the designer tutorial](tutorial-designer-automobile-price-train-score.md).

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