Key takeaways
- Factory-floor model deployment separates where a model is built from where it scores, so each plant runs its predictions next to the line.
- Cisco builds models on H2O AI Managed Cloud and deploys them with H2O eScorer to the factory floor.
- H2O eScorer scores H2O Driverless AI and H2O-3 model pipelines and Python models on standalone instances, outside the platform's own Kubernetes environment.
- Training, versioning and retraining stay in one governed environment while scoring runs at the plant.
The model works in the data center, and the line never sees it
Your data team can build a quality or yield model. Getting that model to score on every line, in every plant, is the part that stalls.
Plant networks are segmented for good reasons. Operational technology sits behind firewalls, some sites run partly offline, and a score that has to travel to the cloud and back arrives after the part has moved on. You are measured on yield, scrap and uptime, and a model that never reaches the line moves none of them.