Model Registration Deployment with Model Registry
With the Amazon SageMaker Model Registry you can do the following:
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Catalog models for production.
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Manage model versions.
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Associate metadata, such as training metrics, with a model.
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View information from Amazon SageMaker Model Cards in your registered models.
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Define a staging construct that models can progress through for your model lifecycle.
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Manage the approval status of a model.
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Deploy models to production.
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Automate model deployment with CI/CD.
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Share models with other users.
Catalog models by creating SageMaker Model Registry Model (Package) Groups that contain different versions of a model. You can create a Model Group that tracks all of the models that you train to solve a particular problem. You can then register each model you train and the Model Registry adds it to the Model Group as a new model version. Lastly, you can create categories of Model Groups by further organizing them into SageMaker Model Registry Collections. A typical workflow might look like the following:
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Create a Model Group.
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Create an ML pipeline that trains a model. For information about SageMaker pipelines, see Pipelines actions.
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For each run of the ML pipeline, create a model version that you register in the Model Group you created in the first step.
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Add your Model Group into one or more Model Registry Collections.
For details about how to create and work with models, model versions, and Model Groups, see Model Registry Models, Model Versions, and Model Groups. Optionally, if you want to further group your Model Groups into Collections, see Model Registry Collections.
