부산대학교 도서관

MLOps, addressing operational issues in machine learning, has gained attention for enhancing the performance of production models. A core challenge is efficiently understanding the causes of mispredictions, as current methods often require labor-intensive manual analysis. To address this, we propose the Extended AI Error Diagnosis Flowchart (eAIEDF) as an extension of the AIEDF, an automated method for identifying root causes of mispredictions during model operation, in order to make it adaptable to both classification and regression models, ensuring applicability in various use cases. Compared to AIEDF, eAIEDF features a more comprehensive flowchart structure for improved cause identification. Through numerical experiments, we confirm that eAIEDF provides valuable insights for enhancing model performance.