优化模型重训策略，节能25%以上同时保持精度。
Sustainable Machine Learning Retraining: Optimizing Energy Efficiency Without Compromising Accuracy
- 仅用最新数据重训，降低能耗25%
- 按需重训（非定时）可再省40%能耗
- 适合关注绿色AI的系统设计者
机器学习系统因数据随时间变化需定期维护，通常通过模型重训实现。但重训计算量大，能源消耗高，带来环境负担。本文研究常见重训技术的能耗表现，在保证精度的前提下进行对比。结果表明：仅使用最新数据重训，相比使用全部历史数据，可降低高达25%的能耗；若结合可靠的数据变化检测器，仅在必要时重训而非固定周期重训，能耗最高可减少40%。这些发现为构建可持续的机器学习系统提供了实证支持，助力开发者选择更节能的重训策略。
原文摘要 · Abstract (English)
The reliability of machine learning (ML) software systems is heavily influenced by changes in data over time. For that reason, ML systems require regular maintenance, typically based on model retraining. However, retraining requires significant computational demand, which makes it energy-intensive and raises concerns about its environmental impact. To understand which retraining techniques should be considered when designing sustainable ML applications, in this work, we study the energy consumption of common retraining techniques. Since the accuracy of ML systems is also essential, we compare retraining techniques in terms of both energy efficiency and accuracy. We showcase that retraining with only the most recent data, compared to all available data, reduces energy consumption by up to 25\%, being a sustainable alternative to the status quo. Furthermore, our findings show that retraining a model only when there is evidence that updates are necessary, rather than on a fixed schedule, can reduce energy consumption by up to 40\%, provided a reliable data change detector is in place. Our findings pave the way for better recommendations for ML practitioners, guiding them toward more energy-efficient retraining techniques when designing sustainable ML software systems.
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