SemML: Reusable ML for condition monitoring in discrete manufacturing

Machine learning (ML) is gaining much attention for data analysis in manufacturing. Despite the success, there is still a number of challenges in widening the scope of ML adoption. The main challenges include the exhausting effort of data integration and lacking of generalisability of developed ML pipelines to diverse data variants, sources, and domain processes. In this demo we present our SemML system that addresses these challenges by enhancing machine learning with semantic technologies: by capturing domain and ML knowledge in ontologies and ontology templates and automating various ML steps using reasoning. During the demo the attendees will experience three cunningly-designed scenarios based on real industrial applications of manufacturing condition monitoring at Bosch, and witness the power of ontologies and templates in enabling reusable ML pipelines.