This course provides a practical foundation for professionals looking to implement AI at the edge of IoT systems. Participants will learn how to design lightweight AI models, deploy them to resource-constrained devices, and optimize performance for real-time decision-making. The training focuses on edge-device limitations, model compression techniques, low-latency processing, and integration with IoT communication protocols—empowering learners to build scalable and intelligent edge solutions.
Day 1: Foundations of Edge AI and IoT
Session 1: Edge Computing and IoT Ecosystem Overview
Architecture of IoT systems
Edge vs. cloud AI: trade-offs and use cases
Constraints of edge environments
Session 2: AI for the Edge – Design Principles
Choosing lightweight models (TinyML, MobileNet, etc.)
Dataset considerations and preprocessing for edge
Day 2: Model Optimization and Deployment
Session 1: Model Compression and Quantization
Techniques: pruning, quantization, distillation
Tools: TensorRT, ONNX, TFLite, Edge Impulse
Session 2: Real-time Deployment on Edge Devices
Microcontrollers, Raspberry Pi, Jetson Nano, Coral
Deploying vision, NLP, and sensor models on hardware
Day 3: Integration, Management, and Use Cases
Session 1: Integrating AI with IoT Protocols
MQTT, CoAP, and edge-cloud communication
Streaming data processing and event-based inference
Session 2: Project Lab and Use Case Simulation
Smart camera, predictive maintenance, anomaly detection
Final project: build and deploy an edge AI pipeline for an IoT application
We are open to customizing this program to align with your specific learning objectives. If your team has particular goals or areas they wish to focus on, we would be happy to tailor the course outline to meet those needs and ensure the program supports the achievement of your desired outcomes.