📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of Edge AI Course | Learn AI Deployment on Edge Device, from foundations to a certified capstone project.
Motivation
• Latency, privacy, bandwidth and offline operation as the real drivers of on-device inference
• The round-trip cost of a cloud call, and the workloads where it is unacceptable
• Where edge is the wrong choice — large models, infrequent inference, abundant connectivity
Compression
• Post-training quantization and quantization-aware training (INT8) and the accuracy they cost
• Pruning and knowledge distillation, and how far each shrinks a network before it breaks
• Reading a size/latency/accuracy trade-off curve instead of chasing one number
Hardware
• MCUs, single-board computers and accelerators — ESP32, Raspberry Pi, Jetson, Coral Edge TPU
• RAM, flash and the operator support that decide whether a model will even load
• TensorFlow Lite, TFLite Micro and ONNX Runtime as the deployment runtimes
Deployment
• Exporting and converting a trained model, then benchmarking real latency and power draw
• Thermal throttling and duty cycles — the reason bench numbers do not hold in the field
• Profiling memory and startup time to find the bottleneck that is not the model
Capstone
• Deploying a vision or keyword-spotting model to a physical device end to end
• Measuring accuracy on-device against the training figures, and explaining the gap
• Over-the-air update and drift monitoring so the deployed model can be maintained
Earn government-registered certification in Edge AI Course | Learn AI Deployment on Edge Device
e-Certificate and e-Marksheet issued on successful completion.
