Xpertech delivered a security-first migration and modernization program on AWS. The AWS Partner implemented a multi-account landing zone using AWS Control Tower for account governance, centralized logging, and guardrails. Xpertech then migrated and modernized workloads in phased waves, using infrastructure automation and standardized runbooks to reduce risk and improve operational consistency.
Key solution components included:
• HIPAA-aligned landing zone with a multi-account structure, centralized audit logging, and policy-driven access controls.
• Private connectivity and network segmentation to support regulated workloads and limit exposure of sensitive systems.
• High-availability deployment for Visage Viewer on Amazon Elastic Compute Cloud (Amazon EC2) with multi–Availability Zone (AZ) design patterns.
• Governed data lake and analytics foundation using Amazon Simple Storage Service (Amazon S3), AWS Lake Formation, AWS Glue, and Amazon Athena.
• Machine learning enablement using Amazon SageMaker to support model development, training, and secure access to curated datasets.
• Operational readiness with dashboards, alerts, and runbooks for incident response, change management, and ongoing optimization.
With the landing zone and standardized migration approach in place, UC San Diego Health improved the reliability and governance of its cloud workloads. Clinical imaging platforms gained resilient architecture patterns and stronger operational visibility, while research and analytics teams gained a governed pathway to ingest EMR-derived and operational datasets into a modern data lake for analytics and machine learning. By using managed AWS services and repeatable automation, the organization reduced manual operational effort, accelerated onboarding of new use cases, and strengthened audit readiness through centralized logging and consistent security controls.
Benefits included:
• Improved resilience for imaging and analytics workloads through multi-AZ design and standardized operational processes.
• Faster delivery of analytics and ML initiatives using governed data lake patterns and managed services.
• Consistent security governance across accounts and workloads with centralized logging, access controls, and encryption defaults.
• Reduced operational overhead through automation, reusable templates, and documented runbooks for cloud operations.
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