Cross-Account ML Service Access Using AWS PrivateLink: Architecture and Governance Models

Abstract

Enterprise organizations increasingly require secure access to centralized machine learning services across multiple AWS accounts while maintaining strict governance controls and operational efficiency. This comprehensive framework introduces a governance-first methodology for implementing cross-account ML service architectures using AWS PrivateLink technology. The proposed framework directly tackles key issues faced when deploying machine learning (ML) within enterprise systems by providing safe network connectivity and removing vulnerabilities associated with being internet-based but still allowing limited access to unique shared ML resources. The framework supports strong Identity and Access Management processes, also includes multi-layered security controls and incorporates compliance auditing capabilities. It also provides capabilities that are required for compliance auditing within the many regulations across diverse industry verticals. The key architectural patterns supported include centralized ML platforms, hub-and-spoke governance models, and hybrid types, allowing for effective use of shared resources while maintaining different security boundaries. The operational strategies supported in the framework include worker nodes with highly sophisticated service discovery, performance auditing and automatic fail-over capabilities to ensure resiliency. Multiregion is taken into consideration with regards to resiliency/disaster recovery, compliance and regulation enforcement through intelligent request routing and data sync capabilities. Cost savings strategies will enable organizations to realize substantial savings in operating costs through capacity planning and resource allocation. The framework also supports the evolution of the organization's ML systems towards serverless and containerized ML platforms, using emerging MLOps capabilities, while still maintaining enterprise security and governance.