A comprehensive, hands-on course that teaches you how to optimize deep learning models for real-world computer vision deployments. You’ll master pruning, quantization, knowledge distillation, neural architecture search, and TensorRT/ONNX compilation — with practical exercises on real architectures like YOLO, ResNet, EfficientNet, and Vision Transformers.

Whether you’re deploying to edge devices, mobile, or cloud GPUs, this course gives you the systematic framework to reduce model size, cut latency, and lower serving costs — without destroying accuracy.

What you’ll learn:
• Structured & unstructured pruning techniques
• Post-training and quantization-aware training (INT8, FP16, mixed precision)
• Knowledge distillation from large teacher models
• ONNX export and TensorRT optimization pipelines
• Profiling and benchmarking inference performance
• Edge deployment strategies (Jetson, mobile, browser)
• Production MLOps patterns for optimized model serving

Includes private community chat, weekly Q&A, and lifetime course updates.