MLOps for Edge AI: Keeping Models Accurate Over Time
Red Hat, Monday, October 26th, 2026
Red Hat explains how to detect drift and retrain edge AI models so they stay accurate after deployment.
The fourth part of Red Hat's edge AI MLOps series covers keeping deployed models accurate as real-world conditions change.
It distinguishes data drift, concept drift, and training-serving skew, each needing different detection, from on-device statistics to human spot checks or shared preprocessing code.
It describes selecting high-value inputs and labeling them through operator overrides, pseudo-labeling, or gen AI-assisted annotation.
Data moves from edge to core via store-and-forward brokers like Kafka or MQTT, and retraining on OpenShift AI with accuracy gates and GitOps canary rollouts closes the loop, with federated learning or local LoRA adapters where data cannot leave a site.