Edge AI, Computer Vision & TinyML
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Curated Certification CHR07
Deploy efficient machine learning on constrained and edge hardware using compression, quantization, GPU acceleration, computer vision, embedded inference, latency/energy measurement and hardware-aware optimization.
Benchmark: Curated source set: MIT HAN Lab + MIT AI Hardware Program + NVIDIA Developer + MIT OpenCourseWare + STMicroelectronics + MIT HAN Lab -- Credential note: this is a Zuyini mastery certificate built from the cited public courses. It is not issued, sponsored or endorsed by the source institutions, providers or companies.
Core Course Stack
1. TinyML and Efficient AI Computing - Fall 2026
2. TinyML and Efficient Deep Learning - 6.S965
3. CUDA Platform - Learn CUDA C++
4. An Even Easier Introduction to CUDA
5. Jetson for AI Education
6. Jetson Nano - AI Tutorials & Hello AI World
7. Robotic Manipulation - Perception & Deep Learning
8. TinyML and Efficient Deep Learning
Hands-On Mastery Capstone
Deploy an image, audio or sensor model to an edge target or constrained simulation. Establish a baseline, apply at least two efficiency techniques, measure accuracy, latency, memory and energy proxies, and explain the hardware/software tradeoffs.
Proof Rubric
- model baseline
- deployment pipeline
- quantization/pruning or acceleration artifact
- edge inference demo
- latency/memory measurements
- accuracy comparison
- failure cases
- optimization decision memo