Lilly Tech Systems
Designing Computer Vision Pipelines at Scale
Build production-grade image and video processing systems that run reliably at scale. Learn to architect CV pipelines for retail checkout, quality inspection, surveillance, and autonomous vehicles — from preprocessing and GPU-accelerated inference to edge deployment, model optimization, and infrastructure that handles thousands of concurrent streams.
Your Learning Path
Follow these lessons in order for a complete understanding of production CV pipeline design, or jump to any topic that interests you.
1. CV Pipeline Architecture
Image processing pipeline components, batch vs real-time CV, edge vs cloud processing, and use case architectures for retail checkout, quality inspection, and surveillance systems.
2. Image Processing Pipeline
Preprocessing (resize, normalize, augment), model inference with YOLO, ResNet, and CLIP, post-processing with NMS and tracking, GPU batching, and full OpenCV + PyTorch pipeline code.
3. Video Processing Architecture
Frame extraction strategies, keyframe detection, hardware-accelerated video decoding with FFmpeg and NVIDIA NVDEC, object tracking with SORT and DeepSORT, and streaming video pipeline code.
4. Model Optimization for Production
TensorRT compilation, ONNX conversion, INT8 quantization for vision models, model pruning, knowledge distillation, and benchmark comparisons with real FPS numbers.
5. Edge Deployment Architecture
NVIDIA Jetson, Intel OpenVINO, mobile deployment with CoreML and TFLite, edge-cloud hybrid architectures, model sync, offline operation, and building an edge inference server.
6. Scaling CV Infrastructure
GPU cluster management for inference, image and video storage with S3 and CDN, result caching, distributed processing with Ray and Dask, and handling 10K+ concurrent streams.
7. Best Practices & Checklist
CV system production checklist, annotation pipeline design, model retraining triggers, and frequently asked questions about building vision systems at scale.
What You'll Learn
By the end of this course, you will be able to:
Design CV Pipelines for Production
Architect end-to-end image and video processing systems that handle real-world scale — from camera ingestion to model inference to result delivery.
Optimize Models for Real-Time Inference
Convert and quantize vision models with TensorRT, ONNX, and OpenVINO to achieve production-grade latency and throughput on GPU and edge hardware.
Deploy CV at the Edge
Build edge inference servers on NVIDIA Jetson, mobile devices, and IoT hardware with offline operation, model sync, and edge-cloud hybrid architectures.
Scale to Thousands of Streams
Design GPU cluster infrastructure, distributed processing pipelines, and storage architectures that handle 10K+ concurrent video streams reliably.