PERFORMANCE TESTING IN MACHINE LEARNING SYSTEMS: A SYSTEMATIC FRAMEWORK FOR EVALUATION AND OPTIMIZATION

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