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Internet of Things (IoT) is transforming the industry by bridging the gapbetween Information Technology (IT) and Operational Technology (OT). Machinesare being integrated with connected sensors and managed by intelligentanalytics applications, accelerating digital transformation and businessoperations. Bringing Machine Learning (ML) to industrial devices is anadvancement aiming to promote the convergence of IT and OT. However, developingan ML application in industrial IoT (IIoT) presents various challenges,including hardware heterogeneity, non-standardized representations of MLmodels, device and ML model compatibility issues, and slow applicationdevelopment. Successful deployment in this area requires a deep understandingof hardware, algorithms, software tools, and applications. Therefore, thispaper presents a framework called Semantic Low-Code Engineering for MLApplications (SeLoC-ML), built on a low-code platform to support the rapiddevelopment of ML applications in IIoT by leveraging Semantic Web technologies.SeLoC-ML enables non-experts to easily model, discover, reuse, and matchmake MLmodels and devices at scale. The project code can be automatically generatedfor deployment on hardware based on the matching results. Developers canbenefit from semantic application templates, called recipes, to fast prototypeend-user applications. The evaluations confirm an engineering effort reductionby a factor of at least three compared to traditional approaches on anindustrial ML classification case study, showing the efficiency and usefulnessof SeLoC-ML. We share the code and welcome any contributions.

SeLoC-ML: Semantic Low-Code Engineering for Machine Learning Applications in Industrial IoT