arXiv:2605.24213v1 Announce Type: cross Abstract: Evaluation harnesses are software systems that orchestrate model evaluation by managing model invocation, data loading, metric computation, and result reporting. Despite their critical role in machine learning infrastructure, their operational challenges and engineering concerns have received limited attention so far. We present an empirical study of 57 evaluation harnesses, deriving a five-stage harness model and classifying 16,560 issues by workflow stage and root cause. Most harness operational challenges concentrate in the Specification sta
The proliferation of machine learning models across various industries necessitates robust and efficient evaluation processes, making the engineering of these systems a critical and timely concern.
This empirical study highlights operational challenges in ML evaluation, which directly impacts the reliability, quality, and regulatory compliance of AI systems, a key concern for strategic actors.
The focus is shifting towards more structured and engineered approaches to ML model evaluation, acknowledging the complexity and importance of the evaluation harness itself.
- · ML infrastructure providers
- · ML operations teams
- · Companies with mature ML pipelines
- · Organizations with ad-hoc ML evaluation
- · Developers neglecting evaluation engineering
Increased investment in specialized tools and platforms for ML model evaluation.
Improved accuracy and robustness of deployed AI models due to better evaluation practices.
Enhanced trust and broader adoption of AI systems in sensitive applications as evaluation rigor increases.
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Read at arXiv cs.LG