PERFORMANCE TESTING IN MACHINELEARNING SYSTEMS: A SYSTEMATICFRAMEWORK FOR EVALUATION ANDOPTIMIZATION

Abstract

Performance testing in machine learning systems presents challenges beyond traditional software testing approaches. This article presents a systematic framework for evaluating and optimizing the performance of machine learning systems in production environments. The article addresses key performance metrics, including latency, throughput, scalability, and resource utilization, while examining the critical challenges of dynamic workloads, hardware dependencies, and data pipeline integration. The article methodology encompasses comprehensive testing approaches, from load testing to resource profiling, and optimization techniques, including model quantization, pruning, and knowledge distillation. Through empirical analysis, The article demonstrates how this framework can be effectively implemented to balance model accuracy with operational efficiency. The article also provides best practices for continuous performance monitoring and optimization in production environments, offering practical guidelines for machine learning practitioners and system architects. The findings contribute to the growing knowledge on deploying efficient and scalable machine learning systems while providing actionable insights for organizations seeking to optimize their ML infrastructure