Model retraining is usually treated as an ongoing maintenance task. But as Harrison Katz now argues, retraining can be better understood as approximate Bayesian inference under computational constraints. The gap between a continuously updated belief state and your frozen deployed model is "learning debt," and the retraining decision is a cost minimization problem with a threshold that falls out of your loss function. In this article Katz provides a decision-theoretic framework for retraining policies. The result is evidence-based triggers that replace calendar schedules and make governance auditable. For readers less familiar with the Bayesian and decision-theoretic language, key terms are defined in a glossary at the end of the article.

翻译：模型重训练通常被视为一项持续的维护任务。但正如Harrison Katz现在所论证的，重训练可以更好地理解为在计算约束下的近似贝叶斯推理。持续更新的信念状态与冻结的已部署模型之间的差距称为"学习债务"，而重训练决策则是一个成本最小化问题，其阈值由损失函数自然推导得出。本文中，Katz为重训练策略提供了一个决策理论框架，其成果是基于证据的触发机制，这些机制取代了日历时间表，并使治理过程具有可审计性。对于不熟悉贝叶斯和决策理论术语的读者，文末附有术语表对关键概念进行定义。