Edge AI Course
Dive deep into edge AI for audio applications: engineer power-efficient models for on-device use, cut down latency and energy use, tackle noisy real-world conditions, and roll out, evaluate, and track sturdy event detection setups on mobiles, IoT gadgets, and compact hardware systems. This course equips you with vital skills for deploying reliable AI at the edge without cloud reliance.

flexible workload of 4 to 360h
valid certificate in your country
What will I learn?
Gain hands-on expertise in creating, fine-tuning, and launching audio event detection models right on edge devices. Master strong data handling, enhancement, and annotation techniques, craft slim model designs, and implement model slimming via pruning, quantisation, and knowledge distillation. Explore device-side processing flows, energy optimisation, practical evaluations, oversight, and secure updates post-launch to keep models performing well in actual scenarios.
Elevify advantages
Develop skills
- Design efficient edge audio models using compact CNNs and transformers for device deployment.
- Optimise model inference on limited hardware with accelerators, batching, and energy-saving methods.
- Prepare reliable audio datasets by cleaning, labelling, and augmenting for edge-based event spotting.
- Compress and quantise models through pruning, distillation, and on-device runtime frameworks.
- Conduct real-world testing with A/B trials, latency checks, and safe deployment updates.
Suggested summary
Before starting, you can change the chapters and the workload. Choose which chapter to start with. Add or remove chapters. Increase or decrease the course workload.What our students say
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