论文标题
生产中模型的监视和解释性
Monitoring and explainability of models in production
论文作者
论文摘要
机器学习生命周期延伸到部署阶段。监视部署的模型对于继续提供支持机器学习的服务至关重要。关键领域包括模型性能和数据监视，使用统计技术检测异常值和数据漂移，并提供了历史预测的解释。我们使用开源工具来讨论在这些领域成功实施解决方案的挑战。
The machine learning lifecycle extends beyond the deployment stage. Monitoring deployed models is crucial for continued provision of high quality machine learning enabled services. Key areas include model performance and data monitoring, detecting outliers and data drift using statistical techniques, and providing explanations of historic predictions. We discuss the challenges to successful implementation of solutions in each of these areas with some recent examples of production ready solutions using open source tools.
