You can define a series of stages that models can progress through for your model workflows and lifecycle with the Model Registry staging construct. This simplifies tracking and managing models as they transition through development, testing, and production stages. The following will provide information on staging constructs and how to use them in your model governance.
The stage construct allows you to define a series of stages and statuses that models progress through. At each stage, specific personas with the relevant permissions can update the stage status. As a model advances through the stages, its metadata is carried forward, providing a comprehensive view of the model's lifecycle. This metadata can be accessed and reviewed by authorized personas at each stage, enabling informed decision making. This includes the following benefits.
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Model Life Cycle Permissions - Set permissions for designated personas to update a model stage state and enforce approval gates at critical transition points. Administrators can assign permission by using IAM policies and condition keys with the API. For example, you can restrict you data scientist from updating the Model Lifecycle stage transition from "Development" to "Production". For examples, see Set up Staging Construct Examples.
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Model Life Cycle Events via Amazon EventBridge - You can consume the lifecycle stage events using EventBridge. This sets you up to receive event notifications when models change approval or staging state, enabling integration with third-party governance tools. See Get event notifications for ModelLifeCycle for an example.
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Search based on Model Life Cycle Fields - You can search and filter stage and stage status using the
SearchAPI. -
Audit Trails for Model Life Cycle Events - You can view the history of model approval and staging events for the model lifecycle transitions.
The following topics will walk you through how to set up a stage construct on the administrator side and how to update a stage status from the user side.
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