A Cyber Manufacturing IoT System for Adaptive Machine Learning Model
  Deployment by Interactive Causality Enabled Self-Labeling
Yutian Ren Yuqi He Xuyin Zhang Aaron Yen G. P. Li
Machine Learning (ML) has been demonstrated to improve productivity in many
manufacturing applications. To host these ML applications, several software and
Industrial Internet of Things (IIoT) systems have been proposed for
manufacturing applications to deploy ML applications and provide real-time
intelligence. Recently, an interactive causality enabled self-labeling method
has been proposed to advance adaptive ML applications in cyber-physical
systems, especially manufacturing, by automatically adapting and personalizing
ML models after deployment to counter data distribution shifts. The unique
features of the self-labeling method require a novel software system to support
dynamism at various levels.
  This paper proposes the AdaptIoT system, comprised of an end-to-end data
streaming pipeline, ML service integration, and an automated self-labeling
service. The self-labeling service consists of causal knowledge bases and
automated full-cycle self-labeling workflows to adapt multiple ML models
simultaneously. AdaptIoT employs a containerized microservice architecture to
deliver a scalable and portable solution for small and medium-sized
manufacturers. A field demonstration of a self-labeling adaptive ML application
is conducted with a makerspace and shows reliable performance.