自动检测数据分布漂移，按需重训模型，节省云资源。
A Multi-Criteria Automated MLOps Pipeline for Cost-Effective Cloud-Based Classifier Retraining in Response to Data Distribution Shifts
- 用多指标统计法识别数据分布变化，仅在必要时触发重训。
- 在多个异常检测数据集上，准确率与鲁棒性显著优于传统方法。
- 适合需要长期维护的实时机器学习系统部署者。
机器学习模型性能常因数据分布随时间变化而下降，即数据分布漂移。此时需重训并重新部署模型。当前MLOps流程多为人工驱动，依赖人力触发重训与部署。本文提出一种自动化MLOps流水线，用于应对神经网络分类器在显著数据分布变化下的重训需求。该流水线采用多准则统计技术检测分布漂移，并仅在必要时触发模型更新，确保计算效率与资源优化。通过在多个基准异常检测数据集上的实验，验证了该框架在模型准确率与鲁棒性方面显著优于传统重训策略。本工作为动态真实场景中部署更可靠、自适应的ML系统提供了基础，尤其适用于数据分布频繁变化的环境。
原文摘要 · Abstract (English)
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.
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