Description
Artificial Intelligence is no longer confined to massive data centers. From predictive maintenance in industrial factories to real-time gesture recognition in wearables, the future of AI is at the edge.
This intensive, hands-on course bridges the gap between high-level machine learning and low-level hardware. Over two days, you will learn how to take a powerful model and “squeeze” it onto resource-constrained hardware like ARM Cortex-M microcontrollers and Single-Board Computers (SBCs) without sacrificing performance.
What You Will Learn
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The Edge Workflow: Master the “Train-Optimize-Deploy” pipeline.
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Hardware Selection: Choosing between MCUs (STM32, ESP32), SBCs (Raspberry Pi), and dedicated AI accelerators (Coral TPU, Jetson Nano).
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Model Compression: Practical techniques for Quantization, Pruning, and Knowledge Distillation.
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Real-Time Inference: Implementing Vision (Object Detection) and Audio (Wake-word) models on the fly.
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Power & Latency: Balancing “Brains vs. Battery”—optimizing for ultra-low power consumption.
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