用强化学习动态管理模型部署，自动回滚表现差的模型。
Reinforcement Learning for Machine Learning Model Deployment: Evaluating Multi-Armed Bandits in ML Ops Environments
- 用多臂赌博机算法实时评估和切换模型
- 在两个真实数据集上表现优于或媲美传统方法
- 适合需要自动化部署的生产环境
在现代机器学习运维环境中，模型部署至关重要，传统方法依赖静态启发式策略，如验证误差比较和A/B测试。但这些方法需人工干预以应对模型漂移或性能意外下降等现实挑战。本文研究强化学习（特别是多臂赌博机，MAB）是否能更有效地动态管理模型部署决策。所提方法通过持续评估已部署模型，并实时回滚表现不佳的模型，实现更具适应性的生产环境。我们在两个真实数据集上测试了六种模型选择策略，发现基于强化学习的方法在性能上达到或超过传统方法。结果表明，基于强化学习的模型管理可提升自动化水平，减少人工干预，降低部署后模型失效的风险。
原文摘要 · Abstract (English)
In modern ML Ops environments, model deployment is a critical process that traditionally relies on static heuristics such as validation error comparisons and A/B testing. However, these methods require human intervention to adapt to real-world deployment challenges, such as model drift or unexpected performance degradation. We investigate whether reinforcement learning, specifically multi-armed bandit (MAB) algorithms, can dynamically manage model deployment decisions more effectively. Our approach enables more adaptive production environments by continuously evaluating deployed models and rolling back underperforming ones in real-time. We test six model selection strategies across two real-world datasets and find that RL based approaches match or exceed traditional methods in performance. Our findings suggest that reinforcement learning (RL)-based model management can improve automation, reduce reliance on manual interventions, and mitigate risks associated with post-deployment model failures.
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