Amazon SageMaker Studio provides a fully managed solution for data scientists to interactively build, train, and deploy machine learning (ML) models. 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. Some common use cases for doing this include:

Regularly running model inference to generate reports
Scaling up a feature engineering step after having tested in Studio against a subset of data on a small instance
Retraining and deploying models on some cadence
Analyzing your team’s Amazon SageMaker usage on a regular cadence

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,

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