Model card FAQs

A: You can use models for a variety of business applications ranging from predicting cyber attacks and approving loan applications to detecting the category of an email. Each of these applications assumes a different level of risk. For example, incorrectly detecting a cyber attack has much greater business impact than incorrectly categorizing an email. Given these varied risk profiles of a model, you can use model cards to provide a risk rating of low, medium, or high for a model. If you don’t know the risk of your model, you can set the status to unknown. Customers are responsible for assigning the risk profile for each model. Based on the risk rating, organizations may have different rules in place for deploying those models to production. For more information, see Risk ratings.

The intended use of a model describes how you should use the model in your production applications. This goes beyond technical requirements like the type of instance to which you should deploy a model and instead refers to the types of applications to create with the model, the scenarios in which you can expect a reasonable performance from the model, or the type of data to use with the model. We recommend providing this information in the model card for better model governance. You can define a kind of model specification in the intended use field and ensure that model developers and consumers follow this specification while training and deploying their models. For more information, see Intended uses of a model.

When you use the SageMaker Python SDK or the AWS console to create your model card, SageMaker AI autopopulates details about your SageMaker AI trained model in the card. This includes details about how the model was trained along with all the model details returned by the describe-model API call.

Amazon SageMaker Model Cards have a defined structure to them that cannot be modified. This structure gives you guidance on what information should be captured in a model card. While you cannot change the structure of the model card, there is some flexibility introduced through custom properties in the Additional information section of the model card.

Model cards have versions associated with them. A given model version is immutable across all attributes other than the model card status. If you make any other changes to the model card, such as evaluation metrics, description, or intended uses, SageMaker AI creates a new version of the model card to reflect the updated information. This is to ensure that a model card, once created, cannot be tampered with.

A: Yes. You can create model cards for models not trained in SageMaker AI, but no information is automatically populated in the card. You must supply all the information needed in the model card for non-SageMaker AI models.

A: Yes. You can export each version of a model card to a PDF, downloaded, and share it.

A: No. You can use model cards independently of the Model Registry.

A: Model cards are intended to provide organizations with a mechanism to document as much detail about their model as they like by following SageMaker AI’s prescriptive guidance along with providing their own custom information. You can introduce model cards at the very start of the ML process and use them to define the business problem that the model should solve and any considerations to think about while using the model. After a model is trained, you can populate the model card associated with that model with information about the model and how it was trained. Model cards are associated with models and are immutable once associated with a model. This ensures that the model card is the single source of truth for all the information related to a model, including how it was trained and how it should be used.

The Model Registry is a catalog that stores metadata about your models. Each entry in the model registry corresponds to a unique model version. That model version contains information about the model such as where the model artifacts are stored in Amazon S3, what container is needed to deploy the model, and custom metadata that should be attached to the model.

A: Model card versions and model versions are different entities in SageMaker AI. Each update to a model card results in a new version of that card. Model versions correspond to incrementally trained models that are registered in the Model Registry. A model card version may be linked to a specific model version in the Model Registry by way of the model ID field in the model card, but this is not necessary.

A: No. You can upload the performance metrics computed by SageMaker Model Monitor to the model card by uploading a metrics file to Amazon S3 and linking that to the card, but there is no native integration between Model Monitor and model cards. Model dashboards are integrated with Model Monitor. For more information on model dashboards, see Amazon SageMaker Model Dashboard.