Exporting your data flow translates the operations that you've made in Data Wrangler and exports it into a Jupyter notebook of Python code that you can modify and run. This can be helpful for integrating the code for your data transformations into your machine learning pipelines.
You can choose any data node in your data flow and export it. Exporting the data node exports the transformation that the node represents and the transformations that precede it.
To export a data flow as a Jupyter notebook
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Navigate to your data flow.
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Choose the ellipsis icon next to the node that you want to export.
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In the context menu, hover over Export, and then hover over Export via Jupyter notebook.
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Choose one of the following:
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SageMaker Pipelines
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Amazon S3
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SageMaker AI Inference Pipeline
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SageMaker AI Feature Store
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Python Code
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The Export data flow as notebook dialog box opens. Select one of the following:
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Download a local copy
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Export to S3 location
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If you selected Export to S3 location, enter the Amazon S3 location to which you want to export the notebook.
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Choose Export.
Your Jupyter notebook should either download to your local machine, or you can find it saved in the Amazon S3 location you specified.
