Amazon SageMaker Model Cards enable you to standardize how models are documented, thereby achieving visibility into the lifecycle of a model, from designing, building, training, and evaluation. Model cards are intended to be a single source of truth for business and technical metadata about the model that can reliably be used for auditing and documentation purposes. They provide a factsheet of the model that is important for model governance.

Until now, model cards were logically associated to a model in the Amazon SageMaker Model Registry using model name match. However, when solving a business problem through a machine learning (ML) model, as customers iterate on the problem, they create multiple versions of the model and they need to operationalize and govern multiple model versions. Therefore, they need the ability to associate a model card to a particular model version.

In this post, we discuss a new feature that supports integrating model cards with the model registry at the deployed model version level. We discuss the solution architecture and best practices for managing model card versions, and walk through how to set up, operationalize, and govern the model card integration with the model version in the model registry.

Solution overview

SageMaker model cards help you standardize documenting your models from a governance perspective, and the SageMaker model registry helps you deploy and operationalize ML models. The model registry supports a hierarchical structure for organizing and storing ML models with model metadata information.

When an organization solves a business problem using ML, such as a customer churn prediction, we recommend the following steps:

  1. Create a model card for the business problem to be solved.
  2. Create a model package group for the business problem to be solved.
  3. Build, train, evaluate, and register the first version of the model package version (for example, Customer Churn V1).
  4. Update the model card linking the model package version to the model card.
  5. As you iterate on new model package version, clone the model card from the previous version and link to the new model package version (for example, Customer Churn V2).

The following figure illustrates how a SageMaker model card integrates with the model registry.

As illustrated in the preceding diagram, the integration of SageMaker model cards and the model registry allows you to associate a model card with a specific model version in the model registry. This enables you to establish a single source of truth for your registered model versions, with comprehensive and standardized documentation across all stages of the model’s journey on SageMaker, facilitating discoverability and promoting governance, compliance, and accountability throughout the model lifecycle.

Best practices for managing model cards

Operating in machine learning with governance is a critical requirement for many enterprise organizations today, notably in highly regulated industries. As part of those requirements, AWS provides several services that enable reliable operation of the ML environment.

SageMaker model cards document critical details about your ML models in a single place for streamlined governance and reporting. Model cards help you capture details such as the intended use and risk rating of a model, training details and metrics, evaluation results and observations, and additional call-outs such as considerations, recommendations, and custom information.

Model cards need to be managed and updated as part of your development process, throughout the ML lifecycle. They are an important part of continuous delivery and pipelines in ML. In the same way that a Well-Architected ML project implements continuous integration and continuous delivery (CI/CD) under the umbrella of MLOps, a continuous ML documentation process is a critical capability in a lot of regulated industries or for higher risk use cases. Model cards are part of the best practices for responsible and transparent ML development.

The following diagram shows how model cards should be part of a development lifecycle.

Consider the following best practices:

  • We recommend creating model cards early in your project lifecycle. In the first phase of the project, when you are working on identifying the business goal and framing the ML problem, you should initiate the creation of the model card. As you work through the different steps of business requirements and important performance metrics, you can create the model card in a draft status and determine the business details and intended uses.
  • As part of your model development lifecycle phase, you should use the model registry to catalog models for production, manage model versions, and associate metadata with a model. The model registry enables lineage tracking.
  • After you have iterated successfully and are ready to deploy your model to production, it’s time to update the model card. In the deployment lifecycle phase, you can update the model details of the model card. You should also update training details, evaluation details, ethical considerations, and caveats and recommendations.

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 creates a new version of the model card to reflect the updated information. This is to ensure that a model card, once created, can’t be tampered with. Additionally, each unique model name can have only one associated model card and it can’t be changed after you create the model card.

ML models are dynamic and workflow automation components enable you to easily scale your ability to build, train, test, and deploy hundreds of models in production, iterate faster, reduce errors due to manual orchestration, and build repeatable mechanisms.

Therefore, the lifecycle of your model cards will look as described in the following diagram. Every time you update your model card through the model lifecycle, you automatically create a new version of the model card. Every time you iterate on a new model version, you create a new model card that can inherit some model card information of the previous model versions and follow the same lifecycle.

Pre-requisites

This post assumes that you already have models in your model registry. If you want to follow along, you can use the following SageMaker example on GitHub to populate your model registry: SageMaker Pipelines integration with Model Monitor and Clarify.

Integrate a model card with the model version in the model registry

In this example, we have the model-monitor-clarify-group package in our model registry.

In this package, two model versions are available.

For this example, we link Version 1 of the model to a new model card. In the model registry, you can see the details for Version 1.

We can now use the new feature in the SageMaker Python SDK. From the sagemaker.model_card ModelPackage module, you can select a specific model version from the model registry that you would like to link the model card to.

You can now create a new model card for the model version and specify the model_package_details parameter with the previous model package retrieved. You need to populate the model card with all the additional details necessary. For this post, we create a simple model card as an example.

You can then use that definition to create a model card using the SageMaker Python SDK.

When loading the model card again, you can see the associated model under "__model_package_details".

You also have the option to update an existing model card with the model_package as shown in the example code snippet below:

my_card = ModelCard.load(("<model_card_name>")
mp_details = ModelPackage.from_model_package_arn("<arn>")
my_card.model_package_details = mp_details
my_card.update()

Finally, when creating or updating a new model package version in an existing model package, if a model card already exists in that model package group, some information such as the business details and intended uses can be carried over to the new model card.

Clean up

Users are responsible for cleaning up resources if created using the notebook mentioned in the pre-requisites section. Please follow the instructions in the notebook to clean up resources.

Conclusion

In this post, we discussed how to integrate a SageMaker model card with a model version in the model registry. We shared the solution architecture with best practices for implementing a model card and showed how to set up and operationalize a model card to improve your model governance posture. We encourage you to try out this solution and share your feedback in the comments section.

About the Authors

Ram VittalRam Vittal is a Principal ML Solutions Architect at AWS. He has over 20 years of experience architecting and building distributed, hybrid, and cloud applications. He is passionate about building secure and scalable AI/ML and big data solutions to help enterprise customers with their cloud adoption and optimization journey to improve their business outcomes. In his spare time, he rides his motorcycle and walks with his 2-year-old sheep-a-doodle!

Natacha Fort is the Government Data Science Lead for Public Sector Australia and New Zealand, Principal SA at AWS. She helps organizations navigate their machine learning journey, supporting them from framing the machine learning problem to deploying into production, all the while making sure the best architecture practices are in place to ensure their success. Natacha focuses with organizations on MLOps and responsible AI.

Source: https://aws.amazon.com/blogs/machine-learning/integrate-amazon-sagemaker-model-cards-with-the-model-registry/