Manufacturing is becoming data-driven, connected, and intelligent.

From visual defect detection to predictive maintenance, AI models now run on factory floors, inspection lines, and embedded edge devices.

Yet most of these systems are fragmented each site runs different versions, lacks governance, and struggles to reproduce results when models or infrastructure change.

This session demonstrates how a portable, governed AI platform built with k0s, KAITO, KitOps, and ClusterAPI brings consistency and compliance to industrial AI.

Using k0s for lightweight Kubernetes at the edge, KAITO for model lifecycle automation, KitOps for signed model artifacts, and ClusterAPI for standardized cluster provisioning, we show how factories can securely deploy and update vision and sensor models anywhere: on-prem, in the cloud, or at the edge, with complete traceability and reproducibility.

Outcome: A blueprint for governed, reproducible AI manufacturing at scale.