Important
As of November 30, 2023, the previous Amazon SageMaker Studio experience is now named Amazon SageMaker Studio Classic. The following section is specific to using the Studio Classic application. For information about using the updated Studio experience, see Amazon SageMaker Studio.
Amazon SageMaker ML Lineage Tracking creates and stores information about the steps of a machine learning (ML) workflow from data preparation to model deployment. With the tracking information, you can reproduce the workflow steps, track model and dataset lineage, and establish model governance and audit standards.
SageMaker AI’s Lineage Tracking feature works in the backend to track all the metadata associated with your model training and deployment workflows. This includes your training jobs, datasets used, pipelines, endpoints, and the actual models. You can query the lineage service at any point to find the exact artifacts used to train a model. Using those artifacts, you can recreate the same ML workflow to reproduce the model as long as you have access to the exact dataset that was used. A trial component tracks the training job. This trial component has all the parameters used as part of the training job. If you don’t need to rerun the entire workflow, you can reproduce the training job to derive the same model.
With SageMaker AI Lineage Tracking data scientists and model builders can do the following:
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Keep a running history of model discovery experiments.
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Establish model governance by tracking model lineage artifacts for auditing and compliance verification.
The following diagram shows an example lineage graph that Amazon SageMaker AI automatically creates in an end-to-end model training and deployment ML workflow.
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