# Manage Registered Models
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Your registered models can be accessed and managed in Amazon SageMaker Unified Studio.

1. Navigate to **AI/ML** > **Models** and select the **Registered Models** tab to view the models.

1. You can review the following information
   + Model group name - Logical grouping for related model versions
   + Model version - Specific version identifier
   + Model artifacts - Location of model files in Amazon S3
   + Description - Optional description of the model and its purpose

1. Select specific models to review key model information like
   + Framework - Machine learning framework used (e.g., PyTorch, TensorFlow)
   + Algorithm - Algorithm or approach used for training
   + Performance metrics - Accuracy, precision, recall, or other relevant metrics

## Model lifecycle management
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The model registry provides version control and lifecycle management:
+ Version tracking - Each model registration creates a new version with unique metadata
+ Approval workflows - Models can have approval status (Approved, Pending, Rejected)
+ Deployment status - Track which versions are deployed to endpoints
+ Model comparison - Compare metrics and metadata across versions

To manage model versions:

1. Choose a model group name to view all versions.

1. Review version details including:
   + Version number - Sequential version identifier
   + Description - Version-specific notes and changes
   + Deployment Status - Current deployment state
   + Approval Status - Workflow approval state
   + Modified Date - Last update timestamp

1. Use the **Actions** dropdown to:
   + Deploy using Notebooks - Use a pre-created notebook to deploy the model to SageMaker AI Endpoint
   + Deploy using Jupyter Notebook - Use a pre-created JupyterLab notebook to deploy the model to SageMaker AI Endpoint
   + Approve/Reject - Update approval status
   + Delete - Delete the selected version