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'aws:ResourceTag/sagemaker:is-scheduling-notebook-job': 'true' To define job definitions that run notebooks on a schedule, you may need to add additional permissions to your SageMaker execution role.įirst, add a trust relationship to your SageMaker execution role that allows to assume your role:

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Be sure to Shut down and Update SageMaker Studio in order to pick up the latest updates.

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For more information on how to upgrade to JupyterLab 3, refer to View and update the JupyterLab version of an app from the console. To use SageMaker notebook jobs, you need to be running a JupyterLab 3 JupyterServer app within Studio. In the following sections, we walk through the steps to create a notebook, parameterize cells, customize additional options, and schedule your job. We utilize the pre-installed SageMaker extension to run notebooks as a job immediately or on a schedule. The following diagram illustrates our solution architecture. In this post, we share how to operationalize your SageMaker Studio notebooks as scheduled notebook jobs.

When the job is complete, you can view the populated notebook cells, including any visualizations! There’s no need for the end-user to modify their existing notebook code. You can run these notebooks on a schedule or immediately. With SageMaker notebook jobs, you can now run your notebooks as is or in a parameterized fashion with just a few simple clicks from the SageMaker Studio or SageMaker Studio Lab interface.

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Previously, when data scientists wanted to take the code they built interactively on notebooks and run them as batch jobs, they were faced with a steep learning curve using Amazon SageMaker Pipelines, AWS Lambda, Amazon EventBridge, or other solutions that are difficult to set up, use, and manage.

Analyzing your team’s Amazon SageMaker usage on a regular cadence.Retraining and deploying models on some cadence.Scaling up a feature engineering step after having tested in Studio against a subset of data on a small instance.Regularly running model inference to generate reports.Some common use cases for doing this include: In addition to the interactive ML experience, data workers also seek solutions to run notebooks as ephemeral jobs without the need to refactor code as Python modules or learn DevOps tools and best practices to automate their deployment infrastructure. Amazon SageMaker Studio provides a fully managed solution for data scientists to interactively build, train, and deploy machine learning (ML) models.

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