Vision engineering from lab to production
MLVision delivers the full stack — model design, training, deployment, and ongoing MLOps — with metrics your leadership and regulators can verify.
Object Detection
Design and deploy custom detectors for manufacturing defects, retail shelf gaps, and safety PPE compliance. We benchmark mAP on your hold-out sets before any production cutover.
- YOLO, DETR, and custom backbone selection
- TensorRT and ONNX optimisation
- REST, gRPC, and Kafka inference sinks
- Bounding-box audit exports for QA
Image Classification
Single- and multi-label classifiers with probability calibration, confusion matrices, and threshold tuning workshops for operations teams.
- Hierarchical taxonomies and label schemas
- Active learning for label-efficient training
- Batch scoring pipelines and webhooks
- Fairness checks across demographic cohorts
Video Analytics
Transform RTSP and file-based feeds into tracked objects, zone violations, and occupancy metrics with frame-accurate event logs.
- Multi-camera ingest and sync
- ByteTrack / DeepSORT integration
- Line-crossing and dwell analytics
- SIEM and VMS webhook connectors
CV Fine-Tuning
Adapt foundation vision models to your domain with documented augmentation policies, learning-rate schedules, and reproducible experiment IDs.
- Transfer learning on proprietary imagery
- Synthetic data and GAN augmentation
- Hyperparameter search with MLflow
- Model cards for compliance review
MLOps for Vision
Governed CI/CD for vision models — registry, canary releases, drift monitoring, and automated rollback when accuracy or latency breaches SLA.
- Kubernetes + Triton deployment patterns
- Embedding-space drift detection
- Canary and blue-green release flows
- 24/7 inference SLO dashboards
Define your vision roadmap
Tell us your camera count, latency targets, and accuracy bar. We respond with a scoped plan — no vague estimates.
Contact MLVision