Computer Vision Production Guide: Pipelines, Moderation, and Edge Deployment

Computer vision production interviews go beyond model accuracy on curated datasets. Interviewers ask about preprocessing pipelines, resolution trade-offs, GPU batching for video streams, and how you handle adversarial or low-quality inputs in the wild.

Task selection drives architecture—classification for moderation, detection for inventory, segmentation for medical imaging each imply different SLAs and eval metrics. Candidates explain when to fine-tune specialized models versus prompt VLMs, and how to validate on production-like noise.

Content moderation and safety vision systems need slice-aware eval across demographics, edge cases, and adversarial uploads. Discuss false positive costs (wrongful blocks) versus false negatives (harmful content live), and human review queues for borderline scores.

Edge deployment introduces quantization, frame sampling, and on-device model updates. Interviewers probe battery and thermal constraints on mobile, model size budgets, and federated telemetry without uploading raw images.

Monitoring visual systems requires drift detection on input distributions—lighting, camera angles, new product packaging—not just output accuracy. Articulate retraining triggers, shadow model evaluation, and rollback when precision drops on critical classes.