Automatic Selection of Retraining Data Based on Misprediction Analysis of Production Models

Abstract

In MLOps, model retraining is crucial for performance improvement. However, existing retraining data selection methods inadequately utilize insights from model operation. This study proposes an adaptive, automatic retraining data selection method using prediction error analysis during model operations. Experiments with synthetic and real-world datasets demonstrate that this method provides stable performance across various data types, making it a promising approach when future data changes are unpredictable.