A new empirical study of 57 machine learning evaluation harnesses reveals significant operational challenges, particularly in the 'Specification' stage where models, datasets, and judges are integrated. The research identified unimplemented features, documentation gaps, and missing input validation as the top three root causes of issues, accounting for over 60% of all problems. These findings advocate for 'Evaluation Engineering' to be recognized as a distinct software engineering discipline, analogous to DevOps. AI
IMPACT Highlights critical infrastructure gaps in ML evaluation, suggesting a need for dedicated engineering practices to improve model deployment and reliability.
RANK_REASON The cluster contains an academic paper detailing an empirical study of ML evaluation harnesses.
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