Edge AI: Deploying AI on Edge Devices Course

Module 1 — Foundations of Edge AI

• Introduction to Edge AI and embedded intelligence
• Difference between cloud AI, fog AI, and edge AI
• Advantages, limitations, and real-world relevance of edge deployment
• Core components of an edge AI ecosystem: sensors, processors, connectivity, and inference engines

Module 2 — Edge Devices and Hardware Platforms

• 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

Module 3 — AI/ML Fundamentals for Edge Deployment

• 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

Module 4 — Data Acquisition and Preprocessing for Edge AI

• 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

Module 5 — Model Optimization for Edge Devices

• 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

Module 6 — Edge AI Deployment Frameworks and Toolchains

• 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

Module 7 — TinyML and Real-Time Edge Intelligence

• 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

Module 8 — Security, Privacy, and Reliability in Edge AI

• 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

Module 9 — Applied Edge AI Projects and Case Studies

• 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