LSPERF19-BP03 Schedule regular performance tests with production-representative data loads

Institute a cadence of regular performance tests using simulated research and clinical data that accurately represents production workloads in terms of volume, variety, and velocity. These scheduled tests should be integrated into your development pipeline to identify performance bottlenecks early, before they impact production operations. Vary test scenarios to account for both typical daily operations and peak usage patterns, such as end-of-month processing or seasonal research activities. Complement scheduled tests with on-demand testing triggered by significant system changes. This proactive approach to performance validation verifies that scientific and clinical users consistently experience responsive systems that meet their demanding computational requirements.

Desired outcome: By implementing a comprehensive performance testing framework, scientific and clinical users experience consistently responsive systems that meet their computational requirements, with zero production incidents caused by performance bottlenecks.

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

Implementation guidance

Implementing effective performance testing for generative AI systems requires establishing a consistent cadence of regular evaluations using carefully constructed datasets that accurately represent production workloads. Organizations should develop simulated research and clinical data that mirrors actual usage patterns in terms of volume, variety, and velocity characteristics, verifying that test results provide meaningful insights about real-world performance. Integrate these scheduled performance tests directly into development pipelines as automated gates, enabling early identification of potential bottlenecks before they can impact production operations or scientific outcomes.

Design a comprehensive testing strategy that incorporates diverse scenarios reflecting both typical daily operations and anticipated peak usage patterns, such as end-of-month processing surges or seasonal research activities that might stress system resources. Complement the regular testing schedule with targeted, on-demand evaluations triggered by significant system changes, including infrastructure updates, algorithm modifications, or data pipeline adjustments.

This proactive, multi-faceted approach to performance validation creates a robust quality framework so that scientific and clinical users consistently experience responsive systems capable of meeting their demanding computational requirements.

Implementation steps