Soummo Bose

Building machine learning models is only a small part of the challenge in heavy-industry environments. The real complexity lies in deploying, scaling, and operating ML systems that must work reliably on the shop floor—often under strict safety, latency, and reliability constraints. This session walks through the end-to-end journey of building production-grade ML systems for heavy industry, covering data acquisition from industrial systems, model development, validation, and deployment into real-world decision workflows. It will highlight how ML models are integrated with existing operational technology (OT) systems, how predictions translate into actionable shop-floor decisions, and how teams handle issues like data drift, model monitoring, explainability, and human-in-the-loop controls. Attendees will gain practical insights into ML system design, MLOps, and decision engineering in industrial settings, along with lessons learned from taking models out of notebooks and into mission-critical production environments.