Towards Evaluation Engineering: An Empirical Study of ML Evaluation Harnesses in the Wild
A new study on machine learning evaluation harnesses reveals significant operational challenges, particularly in integrating external models, datasets, and scoring judges. The research identified over 16,000 issues, with the most common root causes being unimplemented features, documentation gaps, and missing input validation. These findings highlight the need to treat evaluation engineering as a distinct software engineering concern. AI
IMPACT Highlights critical software engineering gaps in ML evaluation, potentially impacting the reliability and efficiency of model deployment.