Amazon SageMaker Pipelines  helps you create fully automated ML workflows from data preparation through model deployment so you can scale to thousands of ML models in production. You can create Pipelines with the SageMaker Python SDK and view, execute, audit them from the visual interface of the SageMaker Studio. SageMaker Pipelines takes care of managing data between steps, packaging the code recipes, and orchestrating their execution, reducing months of coding to a few hours. Every time a workflow executes, a complete record of the data processed and actions taken is kept so data scientists and ML developers can quickly debug problems.