LSPERF03-BP02 Environment isolation by workload type

Implement clear separation between research and clinical environments based on their fundamentally different requirements. Establish a dedicated infrastructure for computationally-intensive research pipelines with burst capacity for parallel processing, while maintaining separate, highly-available environments for clinical applications where consistency and reliability are paramount.

Desired outcome: Create distinct infrastructure environments optimized for the unique requirements of research computing and clinical applications.

Level of risk exposed if this best practice is not established: Medium

Implementation guidance

Implementing clear separation between research and clinical environments is essential for organizations working with generative AI in healthcare and life sciences. Begin by establishing distinct AWS accounts for each environment using AWS Organizations with service control policies (SCPs) that enforce appropriate guardrails. This separation creates natural boundaries for access controls, resource allocation, and regulatory requirements while still enabling cross-account data sharing when necessary through services like AWS RAM.

For research environments, prioritize flexibility and computational power by implementing Amazon SageMaker AI with its comprehensive ML development capabilities. Configure auto scaling compute resources using Amazon EC2 Auto Scaling groups with GPU-accelerated instances like P4d or G5g to support computationally intensive workloads. Implement AWS Batch for efficiently managing parallel processing jobs with spot instances to optimize costs during model training and experimentation phases. This approach provides researchers with the burst capacity needed for iterative development while maintaining cost efficiency.

For clinical environments, focus on high availability and consistency by deploying infrastructure across multiple Availability Zones using AWS CloudFormation or AWS CDK with immutable patterns. Implement Amazon RDS multi-AZ deployments for database reliability and Amazon ElastiCache for consistent performance. Configure detailed monitoring with Amazon CloudWatch and AWS X-Ray for predictable performance characteristics critical for clinical applications. Implement AWS Config rules to enforce configuration adherence with regulatory requirements like HIPAA or GxP.

Establish controlled pathways for promoting validated models from research to clinical environments using AWS CodePipeline with approval gates and validation tests. Store model artifacts in Amazon S3 with versioning enabled and implement AWS Lambda functions to validate model metadata before clinical deployment. Use Service Catalog to create standardized, pre-approved deployment patterns that clinical teams can use without compromising governance requirements.

Implementation steps