AI Model Factory: Scaling AI for Industry 4.0 Applications


  • Dhaval Patel IBM
  • Shuxin Lin IBM Thomas J. Watson Research Center
  • Dhruv Shah IBM Research
  • Srideepika Jayaraman IBM
  • Joern Ploennigs IBM
  • Anuradha Bhamidipati IBM Research
  • Jayant Kalagnanam IBM



AI Model, Automation, Multi-Asset, AI Application, IoT Industry


This demo paper discusses a scalable platform for emerging Data-Driven AI Applications targeted toward predictive maintenance solutions. We propose a common AI software architecture stack for building diverse AI Applications such as Anomaly Detection, Failure Pattern Analysis, Asset Health Forecasting, etc. for more than a 100K industrial assets of similar class. As a part of the AI system demonstration, we have identified the following three key topics for discussion: Scaling model training across multiple assets, Joint execution of multiple AI applications; and Bridge the gap between current open source software tools and the emerging need for AI Applications. To demonstrate the benefits, AI Model Factory has been tested to build the models for various industrial assets such as Wind turbines, Oil wells, etc. The system is deployed on API Hub for demonstration.




How to Cite

Patel, D., Lin, S., Shah, D., Jayaraman, S., Ploennigs, J., Bhamidipati, A., & Kalagnanam, J. (2023). AI Model Factory: Scaling AI for Industry 4.0 Applications. Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16467-16469.