What hardware do you deploy computer vision models on?

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.

How do you handle model accuracy drift in production CV systems?

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.

What is the typical timeline for a computer vision proof of concept?

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.

Can you deploy computer vision in environments without reliable internet?

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.

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

Infra spend ↓29% YoY

Batched edge inference on Jetson cut cloud GPU hours 38% (4,800 → 2,976/yr); cold storage tiering cut the footprint 35% (180TB → 117TB). Detection accuracy held within 1.5 points.

Jetson · DeepStream · S3

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