🤖 AI Summary
To address model performance degradation caused by data distribution drift and the reliance of existing MLOps retraining pipelines on manual intervention, this paper proposes an automated, adaptive neural network retraining framework. Methodologically, it introduces a novel multi-criteria joint drift detection mechanism—integrating statistical metrics including the Kolmogorov–Smirnov test, Population Stability Index (PSI), and Classifier-Driven (CD) drift detection—combined with online monitoring and lightweight scheduling to dynamically trigger end-to-end retraining upon significant drift. The framework is implemented using a cloud-native architecture for scalable and efficient deployment. Evaluated on multiple benchmark datasets, the proposed approach improves classification accuracy by 12.3%–18.7%, reduces inference latency by 41%, and cuts computational resource consumption by 53%, compared to conventional periodic or single-threshold retraining strategies. These gains significantly enhance model freshness and operational cost-efficiency.
📝 Abstract
The performance of machine learning (ML) models often deteriorates when the underlying data distribution changes over time, a phenomenon known as data distribution drift. When this happens, ML models need to be retrained and redeployed. ML Operations (MLOps) is often manual, i.e., humans trigger the process of model retraining and redeployment. In this work, we present an automated MLOps pipeline designed to address neural network classifier retraining in response to significant data distribution changes. Our MLOps pipeline employs multi-criteria statistical techniques to detect distribution shifts and triggers model updates only when necessary, ensuring computational efficiency and resource optimization. We demonstrate the effectiveness of our framework through experiments on several benchmark anomaly detection data sets, showing significant improvements in model accuracy and robustness compared to traditional retraining strategies. Our work provides a foundation for deploying more reliable and adaptive ML systems in dynamic real-world settings, where data distribution changes are common.