LSPERF19-BP01 Implement infrastructure as code for consistent test environments
Use infrastructure as code (IaC) to deploy consistent, repeatable test environments that accurately mirror production. By codifying your infrastructure, you reduce configuration drift between test and production environments, This approach enables teams to rapidly provision test environments on demand, run comprehensive load tests against scientific workflows, and tear down resources when testing concludes.
Desired outcome: Implement infrastructure as code (IaC) to deploy consistent, repeatable test environments that mirror production, reducing configuration drift and enabling on-demand provisioning for comprehensive scientific workflow load testing with efficient resource management.
Level of risk exposed if this best practice is not established: Medium
Implementation guidance
Implementing IaC provides a foundation for creating consistent, repeatable test environments that precisely mirror production configurations for generative AI systems. By codifying infrastructure specifications, organizations can reduce configuration drift between testing and production environments and verify that performance evaluations and validation tests accurately reflect real-world conditions. This approach enables development and scientific teams to rapidly provision complete test environments on demand through automated processes, significantly reducing setup time and reducing manual configuration errors.
Teams can execute comprehensive load tests against scientific workflows in these environments, gathering performance metrics that reliably predict production behavior while maintaining strict validation controls. The ephemeral nature of IaC-provisioned environments allows organizations to tear down resources immediately after testing concludes, optimizing cost efficiency without sacrificing testing rigor.
Implement version control for infrastructure code to maintain historical records of environment configurations, supporting audit requirements and enabling rollback capabilities when needed.
Implementation steps
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Use AWS CloudFormation to codify your generative AI infrastructure stack.
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Implement AWS Config to detect and avoid configuration drift.
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Deploy Service Catalog to standardize environment provisioning.
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Integrate Distributed Load Testing on AWS for scientific workflow validation.
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Configure Amazon EventBridge to automate resource cleanup after testing.