Annotation to edge inference to fleet-wide monitoring, models validated like products.
Make AI repeatable and fleet-scalable, an end-to-end MLOps pipeline from dataset creation through edge deployment, with CI-gated validation so the best model ships under a release.
Why teams choose it
Repeatable
From dataset to deployment, every time.
Validated like a product
CI/Git-triggered validation reports per release.
Edge-ready
Optimized for on-device inference.
What's included
Dataset & annotation
Intel CVAT for high-quality labeled datasets.
Train & optimize
TensorFlow training; OpenVINO IR conversion.
Validate & monitor
CI-triggered validation and fleet monitoring.
How it works
01
Annotate
Create labeled datasets with CVAT.
02
Train & convert
TensorFlow to OpenVINO IR for edge performance.
03
Validate & deploy
CI/Git-triggered validation picks the release model.
What powers it
Powered by our MLOps engineering: CVAT → TensorFlow → OpenVINO IR with CI/Git-triggered validation reports, the spine that makes edge AI repeatable across a fleet.
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