论文标题
物联网的AI时间序列模型的可扩展部署
Scalable Deployment of AI Time-series Models for IoT
论文作者
论文摘要
IBM Research Castor是一种用于在物联网应用程序中管理和部署大量AI时间序列模型的云本地系统。在典型的机器学习工作流程后，在Python和R中建模代码模板。一种基于知识的方法来管理模型和时间序列数据，允许使用一般语义概念来表达特征工程任务。模型模板可以通过语义概念的特定实例进行编程部署，从而支持模型重复使用和随着物联网应用程序的增长而自动复制。部署的模型将在并行利用无服务器云计算框架的情况下自动执行。经过训练的模型版本和滚动马预测的完整历史记录持续存在，从而实现了完整的模型谱系和可追溯性。报告了现实世界中智能网格实时预测应用程序中部署的结果。还评估了执行多达数万个AI建模任务的可伸缩性。
IBM Research Castor, a cloud-native system for managing and deploying large numbers of AI time-series models in IoT applications, is described. Modelling code templates, in Python and R, following a typical machine-learning workflow are supported. A knowledge-based approach to managing model and time-series data allows the use of general semantic concepts for expressing feature engineering tasks. Model templates can be programmatically deployed against specific instances of semantic concepts, thus supporting model reuse and automated replication as the IoT application grows. Deployed models are automatically executed in parallel leveraging a serverless cloud computing framework. The complete history of trained model versions and rolling-horizon predictions is persisted, thus enabling full model lineage and traceability. Results from deployments in real-world smart-grid live forecasting applications are reported. Scalability of executing up to tens of thousands of AI modelling tasks is also evaluated.
