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.
翻译:IBM 研究卡斯特(IBM Research Castor) 是一个云型系统,用于在IoT应用中管理和部署大量AI时间序列模型。模型代码模板(Python和R),在典型的机器学习工作流程之后得到支持。基于知识的模型和时间序列数据管理方法允许使用一般语义概念来表达特征工程任务。模型模板可以针对语义概念的具体实例在程序上部署,从而随着 IoT 应用的增长支持模型的再利用和自动复制。部署的模型在利用一个没有服务器的云计算框架的同时自动执行。经过培训的模型版本和滚动焦里松预测的完整历史持续存在,从而使得完整的模型线条和可追溯性得以延续。报告在现实世界智能电网中部署的现场预测应用的结果。还评估了执行高达数万个AI模型任务的规模。