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Overview of edge hardware architectures • Microcontrollers, embedded systems, SoCs, GPUs, TPUs, and NPUs • Comparative study of popular platforms: Raspberry Pi, NVIDIA Jetson, Arduino, Coral, ESP32 • Hardware selection criteria for AI deployment

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Refresher on machine learning and deep learning concepts • Model types commonly used in edge AI: CNNs, RNNs, Transformers, TinyML models • Training vs inference: understanding deployment constraints • Performance metrics for edge intelligence: latency, accuracy, memory, and power consumption

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Sensor data collection and real-time input streams • Data preprocessing pipelines for image, audio, video, and time-series data • Feature engineering for resource-constrained environments • Handling noisy, incomplete, and streaming data at the edge

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Model compression techniques: pruning, quantization, and knowledge distillation • Lightweight neural network architectures for edge deployment • Trade-offs between model size, speed, and accuracy • Optimization tools and frameworks for efficient inference

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Introduction to TensorFlow Lite, TensorRT, ONNX Runtime, OpenVINO, and Edge Impulse • Model conversion and compatibility across platforms • Building end-to-end deployment pipelines • Debugging, benchmarking, and monitoring deployed models

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Fundamentals of TinyML for microcontroller-based AI • Real-time inference on low-power embedded systems • Event-driven AI applications on constrained devices • TinyML use cases in healthcare, agriculture, manufacturing, and smart systems

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Security challenges in edge AI systems • Privacy-preserving AI and on-device intelligence • Robustness, fault tolerance, and adversarial considerations • Ethical and regulatory concerns in edge deployment

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Computer vision applications on edge devices • Predictive maintenance and industrial monitoring • Smart healthcare and wearable AI systems • Reproducible hands-on deployment workflow using Python and embedded platforms