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

A comprehensive, hands-on course for ML engineers who need their models to run faster, smaller, and cheaper in production. You'll learn the full optimization pipeline — from profiling bottlenecks to deploying quantized and pruned models on real hardware. Every module includes runnable code, real benchmarks, and techniques pulled directly from production computer vision systems. **What you'll master:** • Model profiling & bottleneck analysis (latency, memory, FLOPs) • Post-training quantization (INT8, FP16, mixed-precision) • Quantization-aware training for accuracy recovery • Structured & unstructured pruning strategies • Knowledge distillation — teacher-student architectures • ONNX Runtime, TensorRT, and OpenVINO export pipelines • Edge deployment (Jetson, mobile, browser via WASM) • Serving infrastructure — batching, caching, autoscaling **Who this is for:** ML engineers, CV developers, and applied researchers who have working models but need them production-ready. You should be comfortable with PyTorch. **What you get:** → Full course with structured chapters and video lessons → Community chat with direct instructor access → Weekly updates with new optimization techniques and benchmarks

Deep Learning Model Optimization currently has 1 active member and is priced at $289/week.

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