Deep Learning Model Optimization is an ai ml education product available on the Whop marketplace, created by emillashay and launched in June 2026.

A comprehensive, hands-on course designed for computer vision engineers who need to deploy deep learning models in production. You'll master the complete optimization pipeline — from profiling bottlenecks and applying pruning/quantization techniques, to compiling models with TensorRT and deploying on edge devices. This isn't theory-heavy academia. Every module includes real-world code walkthroughs, benchmark comparisons, and production-ready workflows you can apply immediately to your own models. **What you'll learn:** • Profile and identify computational bottlenecks in CNN and transformer architectures • Apply structured/unstructured pruning to reduce model size by 50-90% • Implement INT8/FP16 quantization with minimal accuracy loss • Use knowledge distillation to train compact student models • Optimize inference with ONNX Runtime, TensorRT, and OpenVINO • Deploy optimized models on edge devices (Jetson, mobile, browser) • Build automated optimization pipelines for CI/CD **Who this is for:** • ML/CV engineers moving models from research to production • Software engineers working with real-time vision systems • Anyone deploying models where latency, memory, or cost matters New lessons drop weekly. Community access included for peer support and code reviews.

Deep Learning Model Optimization currently has 1 active member and is priced at $291/week.

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