# Model Registration Deployment with Model Registry
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With the Amazon SageMaker Model Registry you can do the following:
+ Catalog models for production.
+ Manage model versions.
+ Associate metadata, such as training metrics, with a model.
+ View information from Amazon SageMaker Model Cards in your registered models. 
+ View model lineage for traceability and reproducibility.
+ Define a staging construct that models can progress through for your model lifecycle.
+ Manage the approval status of a model.
+ Deploy models to production.
+ Automate model deployment with CI/CD.
+ 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:
+ Create a Model Group.
+ Create an ML pipeline that trains a model. For information about SageMaker pipelines, see [Pipelines actions](pipelines-build.md).
+ For each run of the ML pipeline, create a model version that you register in the Model Group you created in the first step.
+ 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](model-registry-models.md). Optionally, if you want to further group your Model Groups into Collections, see [Model Registry Collections](modelcollections.md).