Computer Vision
Vision model deployment, real-time inference pipelines, and edge ML patterns.
Where the latency budget actually goes
Teams new to production CV usually optimize the model and ignore the pipeline around it. In practice, decode, resize, and pre/post-processing frequently cost more wall-clock time than inference itself, especially on edge hardware. Profile the full frame-to-decision path before deciding the model needs to get smaller.
Deployment targets we design for
The right runtime depends entirely on where inference happens: server-side batch inference tolerates larger models and higher latency; real-time server inference (e.g. live video moderation) needs GPU-backed autoscaling with strict p99 latency SLOs; on-device/edge inference needs a quantized, hardware-specific export (TensorRT, Core ML, TFLite) and a fallback path for when the model is stale or unavailable.
- Server batch — cost-optimized, latency-tolerant workloads
- Server real-time — GPU autoscaling, strict p99 budgets
- Edge/on-device — quantized export, offline-first fallback
Data drift is the silent failure mode
Vision models degrade quietly when camera hardware, lighting, or scene composition shifts after launch. We build in a lightweight drift signal — tracking prediction-confidence distributions over time — so a silent accuracy drop shows up as an alert instead of a support ticket three months later.
Working through something like this?
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