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Deploying puts a model to work on real devices. Where promoting a champion sets your project’s production model across the fleet, Deploy lets you push a specific model to a specific group of devices.

Deploy a model

  1. Open Models and select Deploy on the model you want to ship.
  2. Choose the group to deploy to — the model rolls out to every device in it.
  3. Confirm. Each device picks up the model on its next check-in.
Deploy to a group dialog

Deploy a model to a group — every device in the group picks it up.

Detection labels come straight from the dataset’s classes — no extra setup.

Watch it run

Open Video to see live inference on your devices. Detections appear in real time, with Good / Defect events listed alongside the stream.
Live inference

Live detections and events, straight from the device.

Need a different runtime? Models export to PyTorch, ONNX, TFLite, and Synaptics NPU (SyNAP) formats for edge hardware. See Edge agent for what runs where.

You’ve completed the loop

From here, collecting more data and retraining follows the same path — that’s how you adapt a model as the real world changes.

Operate your fleet

Manage and monitor the devices running your models.

Watch for drift

Catch when a model’s live behavior starts to slip.