Reseña del libro "Beyond the Benchmark (en Inglés)"
A model can be impressive and still be wrong for the job.Every week brings a new leaderboard, a new record and a new claim that one system is simply better. Beyond the Benchmark gives you the language, the mathematics and the judgment to tell whether any of it applies to your decision.This practical guide moves from classical prediction metrics to language models, retrieval, agents, safety, efficiency and live deployment. You do not need advanced mathematics: every formula is paired with an interpretation and a small worked example, and readers with technical experience can use the same pages as a design checklist.Inside you will learn how to:Turn a vague claim such as "better AI" into a testable question with a task, a test and a limitDesign an experiment with honest baselines, representative data and splits that do not leak their answersRead accuracy, precision, recall, thresholds, calibration, uncertainty, ranking and clustering metrics with confidenceJudge whether a lead is real using intervals, paired tests, sample size and corrections for multiple comparisonsEvaluate language models, retrieval pipelines, agents and whole systems rather than isolated componentsTest trustworthiness: robustness, fairness, safety and privacy as launch gates, not afterthoughtsMeasure the real cost of a system in latency, money and human review timeMonitor quality in production and write a decision that others can challengeA complete worked case study runs through candidate selection end to end, and a reference shelf closes the book with a glossary, the essential formulas, a one-page evaluation plan and pointers to primary sources.Good evaluation does not remove uncertainty. It makes uncertainty visible enough to act responsibly. By the last page you should be able to read a leaderboard without mistaking it for a purchasing decision, design a test that does not fool you, and explain why a model suits a particular job without claiming it is universally best.For engineers, data scientists, product managers, technical leaders and anyone who has to decide which AI system to trust. Read more
