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Evaluation is the hardest engineering problem in production ML, often answering the wrong question. Rebuilt ImageNet test sets dropped model accuracy 11 to 14%. Evaluation frameworks need versioned data, failure-mode coverage, and reproducible runs to gate deployments responsibly.
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