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Merge pull request #6493 from s-polly/main
batch endpoint jobs - mltable note
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articles/machine-learning/how-to-access-data-batch-endpoints-jobs.md

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@@ -341,7 +341,7 @@ The following examples show how to create jobs while taking data inputs from [da
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Azure Machine Learning data assets (formerly known as datasets) are supported as inputs for jobs. Follow these steps to run a batch endpoint job that uses input data that's stored in a registered data asset in Azure Machine Learning.
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> [!WARNING]
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> Data assets of type table (`MLTable`) aren't currently supported.
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> Data assets of type table (MLTable) aren't currently supported for model deployments. MLTable is supported for pipeline component deployments.
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1. Create the data asset. In this example, it consists of a folder that contains multiple CSV files. You use batch endpoints to process the files in parallel. You can skip this step if your data is already registered as a data asset.
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