How to Deploy Computer Vision Behind a Data Diode
Blog post from Roboflow
Deploying Roboflow computer vision behind a data diode keeps camera feeds, model execution, and production controls inside a protected network while allowing approved results such as defect detections, metrics, and equipment status to flow outward. Roboflow Inference can operate locally through Enterprise Offline Mode, which caches model weights and Workflow definitions so runtime processing does not require cloud access, although cached model licenses must still be renewed within a 30-day lease period through planned, separate two-way maintenance access. Because a true diode cannot support TCP-based sessions or acknowledgements across the boundary, MQTT connections must terminate at separate brokers on each side, with a forwarding process carrying serialized, self-contained messages through a one-way transport. UDP may also be suitable for one-way prediction delivery, but reliability mechanisms such as sequence numbers or redundancy must be added separately if needed. Monitoring similarly shifts from external polling to outbound reporting: internal processes can publish regular health heartbeats containing status, timestamps, and inference activity, while receiving systems detect faults when expected messages stop arriving. Successful deployments should verify offline startup, one-way-only output, separated MQTT sessions, planned licensing renewal, and health visibility based solely on outbound signals.
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