With the increasing adoption of Artificial Intelligence (AI) and Machine Learning (ML) technologies in enterprises, it has become crucial to implement proper guardrails throughout the ML lifecycle. These guardrails ensure the security, privacy, and quality of the code, configuration, data, and model used in ML models. However, implementing these guardrails has become more complex due to the involvement of multiple stakeholders and operational processes.
To address these challenges, AWS has introduced new ML governance tools for Amazon SageMaker, which simplify access control and enhance transparency over ML projects. One of these tools is Amazon SageMaker Model Cards, which centralizes and standardizes documentation throughout the model lifecycle. Model cards provide a single source of truth for business and technical metadata, enabling auditing and documentation.
To ensure scalability, it is recommended to adopt a multi-account strategy for ML model development and deployment. The architecture consists of four accounts: Data Science Account, ML Shared Services Account, Dev Account, and Data Account. CI/CD pipelines are used to automate the ML lifecycle, and services like AWS IAM, AWS CloudTrail, and AWS Security Hub are utilized for security and governance.
To improve visibility and governance of ML models, AWS has introduced cross-account model card sharing. This feature allows customers to share model cards across accounts, enabling collaboration and ensuring governance. The architecture includes Lead Data Scientists creating model cards in the ML Shared Services Account, which are then shared with the ML Dev Account, ML Test Account, and ML Prod Account based on the model card status.
To set up cross-account model card sharing, users in the model card account can share model cards with shared accounts using AWS RAM. AWS RAM helps in sharing resources across AWS accounts. Users can create a model card in the model card account and then create a cross-account share using AWS RAM. The shared model cards can be accessed and updated by users in the shared accounts.
In summary, the introduction of ML governance tools and cross-account model card sharing in Amazon SageMaker simplifies access control, enhances transparency, and improves governance over ML models in enterprises. These features enable collaboration, scalability, and ensure the security and quality of ML models throughout their lifecycle.