Computer Vision FastTrack
Takes a computer-vision model from proof-of-concept to production on edge or cloud, with measured precision, low-latency inference, and drift monitoring.
- Timeline
- 3-4 weeks (PoC), 8 weeks (production)
- Team
- CV lead, MLE, edge dev, FE, QA; ops reviewer for deployment
- Typical stack
- Edge hardware: NVIDIA Jetson (Nano/Xavier/Orin), x86 (Intel/AMD), ARM (RPi/custom). Models: YOLOv8/v11, EfficientDet, custom CNNs; ONNX/TensorRT export; INT8 quantization. Frameworks: PyTorch/TensorFlow → ONNX; TensorRT optimization for 3-5x speedup. Streaming: RTSP/RTMP/USB; frame buffering and batching; resilient reconnect with backoff; timestamp sync with clock drift checks. Deployment: Docker/K3s on edge; REST/MQTT for results; centralized model registry. Edge management: heat throttling guard; GPU temp monitoring; rolling buffers with cold tier (S3/Blob) lifecycle rules. MLOps: Prometheus/Grafana for drift; Label Studio/CVAT for re-labeling; DVC for dataset versioning. Cloud fallback: AWS/GCP/Azure for heavy inference or batch processing.
What you get
- Custom model trained on your footage (detection/tracking/classification)
- Edge deployment package: ONNX/TensorRT optimized for Jetson/x86/ARM
- Inference pipeline with <target latency (typically 60-200ms p95)
- Precision/recall benchmarks on test set with confusion matrices
- MLOps workflow: drift monitoring, review UI, re-labeling, retraining hooks
- Production runbook: deployment, rollback, troubleshooting, scaling
Outcomes
- Model meets precision/recall target on your footage
- Edge pipeline ≤ target latency; stable FPS
- Ops workflow (review, re-label, retrain) live
- Measurable reduction in manual review time (typically 87%)
- Drift monitoring and automated retraining triggers
Selected work
How we approach it
Edge-Only
- When:
- On-prem requirements, no cloud connectivity, <100ms latency needed
- Tradeoffs:
- Lower cost, data sovereignty, limited model size, manual updates
- Best for:
- Government/DOT, utilities, remote sites
Hybrid Edge + Cloud
- When:
- Real-time inference at edge + batch analytics in cloud
- Tradeoffs:
- Best of both worlds, resilient to connectivity loss, moderate complexity
- Best for:
- Manufacturing, retail, logistics
Cloud-Fallback
- When:
- Primary cloud inference with edge backup during outages
- Tradeoffs:
- Simpler edge footprint, higher latency, cloud dependency
- Best for:
- Enterprise with reliable connectivity
Where teams use it
Manufacturing
Defect detection & assembly verification
Identify surface defects, misalignments, and missing components on production lines with 96%+ accuracy
Transportation & Rail
Track/ROW inspection & rolling stock monitoring
Automated detection of rail surface defects, vegetation encroachment, and equipment anomalies reducing manual inspection time by 87%
Warehousing & Logistics
Package counting, damage detection & compliance
Real-time package damage detection and volumetric counting with sub-second latency for high-throughput operations
Retail
Shelf compliance & queue management
Monitor product placement, planogram compliance, and customer queue depth for operational optimization
What we need from you
- Sample footage or image sets (200-500 frames minimum)
- Labeling guidelines or existing annotations
- Target edge hardware specs (Jetson/x86/ARM)
- Precision/recall targets and acceptable latency
- Ops workflow requirements (drift alerts, retraining triggers)
Proof points
- Precision/recall scorecard with per-class breakdown
- Latency benchmarks (p50/p95/p99) across hardware configs
- FPS stability chart (30-day post-deployment)
- Drift detection alert examples with remediation
- Manual review time reduction (before/after workflow analysis)
- Model card with architecture, inputs, outputs, limitations
- Production model inference p95 latency ≤ 200ms
Built for procurement
- Accuracy thresholds: Precision/recall targets per class with confusion matrices
- Latency SLAs: p50/p95/p99 inference latency with hardware specs
- Audit trails: Chain-of-custody for training data, model versions, and predictions
- Evidence packs: Exportable detection results with timestamps, confidence scores, and visual proof
- Retention windows: Configurable data retention policies (RTSP streams, predictions, re-training sets)
- Redaction options: PII masking for faces, license plates, and identifiable features
- Compliance: CJIS-compliant for law enforcement, SOC 2 Type II, on-prem/GovCloud deployment options
Frequently asked questions
It depends on your latency and cost requirements. NVIDIA Jetson devices (Orin, Xavier) handle most edge deployments where models run on-site. For higher throughput, x86 servers with dedicated GPUs work better. We profile your specific model and data volume to recommend the right hardware before writing any code.
We build automated drift monitoring that compares live inference distributions against your baseline metrics. When accuracy drops below your threshold, the system alerts your team and can trigger retraining pipelines with newly collected data. This is MLOps for computer vision, not a train-once-and-forget approach.
Allerin's FastTrack program delivers a working PoC in 3 to 4 weeks, with the full production deployment completing by week 8. The PoC includes model selection, training on your data, edge optimization, and measured accuracy benchmarks against your success criteria.
Yes, that is what edge deployment means. The model runs entirely on local hardware. Inference happens on-device with zero cloud dependency. Results sync to your central system when connectivity is available. This architecture is critical for manufacturing floors, field operations, and locations with air-gapped networks.