Edge AI Course | Learn AI Deployment on Edge Device

Module 1: Introduction to Edge AI

• Overview of Edge AI and edge computing concepts
• Difference between cloud AI, edge AI, and embedded AI
• Real-world applications of Edge AI in IoT, healthcare, automation, and smart devices

Module 2: Fundamentals of AI Model Deployment

• Understanding the AI model development and deployment lifecycle
• Preparing trained AI models for real-world deployment
• Challenges of deploying AI models on low-power and resource-limited devices

Module 3: Edge Devices and Hardware Platforms

• Introduction to edge devices, microcontrollers, sensors, and embedded systems
• Overview of Raspberry Pi, Arduino, ESP32, Jetson Nano, and mobile devices
• Choosing the right hardware platform for Edge AI applications

Module 4: Model Optimization for Edge Deployment

• Need for model optimization in Edge AI systems
• Introduction to quantization, pruning, and model compression
• Balancing accuracy, latency, memory usage, and power consumption

Module 5: TensorFlow Lite for Edge AI

• Introduction to TensorFlow Lite and lightweight AI deployment
• Converting trained models into TensorFlow Lite format
• Running optimized inference on mobile, embedded, and IoT devices

Module 6: TinyML and AI on Microcontrollers

• Introduction to TinyML and ultra-low-power machine learning
• Deploying simple AI models on microcontrollers and sensor-based systems
• Use cases in keyword spotting, anomaly detection, and sensor intelligence

Module 7: ONNX Runtime and Cross-Platform AI Deployment

• Introduction to ONNX and model interoperability
• Using ONNX Runtime for deploying AI models across different platforms
• Understanding cross-framework deployment for Edge AI applications

Module 8: Edge AI for Computer Vision Applications

• Deploying image classification and object detection models on edge devices
• Real-time computer vision using cameras and embedded AI platforms
• Applications in surveillance, robotics, agriculture, and quality inspection

Module 9: Edge AI for IoT and Sensor Data

• Using AI for real-time analysis of sensor and IoT data
• Edge-based anomaly detection, predictive maintenance, and event detection
• Integrating AI models with IoT workflows and connected devices

Module 10: Security, Privacy, and Performance in Edge AI

• Understanding privacy benefits of local AI inference
• Security challenges in connected edge and IoT devices
• Performance monitoring, power efficiency, and deployment reliability

Module 11: Capstone Project and Real-World Edge AI Workflow

• Designing an end-to-end Edge AI deployment workflow
• Optimizing and deploying a lightweight AI model for practical use
• Presenting project outcomes, deployment challenges, and improvement strategies