Evaluating AI System Performance Questions

Focuses on how to measure and benchmark the effectiveness, efficiency, and reliability of AI models and end to end systems. Covers task specific evaluation metrics such as precision, recall, F one score for classification; mean squared error and mean absolute error for regression; perplexity and translation quality metrics for natural language processing; and intersection over union for computer vision. Emphasizes using multiple complementary metrics rather than a single accuracy number, calibration and uncertainty estimation, statistical significance and confidence intervals, and appropriate validation strategies such as cross validation and held out test sets. Includes system level and operational metrics like latency, throughput, memory usage, computational cost, and overall cost versus performance trade offs. Also covers benchmarking methodology, baseline selection, dataset curation for fair comparison, handling distribution shift, continuous evaluation and monitoring in production, instrumentation for performance measurement, and reasoning about asymptotic complexity and inference cost when evaluating deployment feasibility.