Build production-grade computer vision systems. From image classification to real-time object detection with YOLO, semantic segmentation, edge deployment, and video analysis pipelines.
Production image classification systems. Modern architectures (EfficientNet, ConvNeXt, Vision Transformers), transfer learning strategies, data augmentation, and handling imbalanced datasets.
Real-time object detection with YOLO (v8/v9/v10) and Faster R-CNN. Anchor-based vs. anchor-free detectors, NMS, model selection, and training custom detectors on your own data.
Pixel-level classification with U-Net, DeepLab, and Segment Anything (SAM). Instance segmentation, panoptic segmentation, and building segmentation pipelines for real-world applications.
Process video streams in real-time. Object tracking (SORT, DeepSORT, ByteTrack), action recognition, temporal analysis, and building video analytics pipelines that scale.
Deploy CV models on edge devices. ONNX export, TensorRT optimisation, quantisation, and running inference on NVIDIA Jetson, Intel OpenVINO, and mobile devices.
Build low-latency inference systems. Batching strategies, GPU memory management, model serving with Triton, concurrent request handling, and meeting SLA requirements.
Advanced augmentation: Albumentations, CutMix, MixUp, mosaic augmentation, synthetic data generation with diffusion models, and building augmentation pipelines that actually improve performance.
End-to-end CV pipelines: data labelling workflows, active learning, continuous training, model versioning, A/B testing, and monitoring model quality in production.
ML engineers who want to specialise in computer vision and deploy CV models in production
Software engineers building products that require image or video analysis capabilities
Robotics and IoT engineers who need to deploy vision models on edge devices
Python and basic deep learning knowledge required. You should be comfortable with PyTorch, understand CNNs, and have trained neural networks before. Our Deep Learning Masterclass provides ideal preparation.
Interactive sessions with real-time Q&A and screen sharing
All sessions recorded and available for 12 months after the course
Real-world projects that build your portfolio as you learn
Personal mentoring sessions to address your specific questions
Industry-recognised certificate upon successful completion
Our instructors are seasoned practitioners with years of experience building production AI systems. They hold certifications across major cloud platforms and have trained thousands of professionals worldwide.
GPU access is provided through cloud platforms for all lab sessions. For edge deployment labs, we provide remote access to NVIDIA Jetson devices. A standard laptop is sufficient for the course.
You will build a real-time object detection system, a video analytics pipeline, and deploy a model to an edge device. Each project is designed to mirror real industry applications.
Yes. The techniques (classification, segmentation, detection) are domain-agnostic. We cover domain adaptation and provide guidance for specialised applications including medical and geospatial imaging.
Pre-built APIs are great for common tasks but fall short for custom needs. This course teaches you to build, train, and deploy custom models -- giving you flexibility, better accuracy on your data, and lower per-inference costs at scale.