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Duration 21 hours
Course Outline
Introduction to Edge AI in Industrial Contexts
- The strategic significance of edge computing in manufacturing
- Comparison against cloud-centric AI architectures
- Applications in visual inspection, predictive maintenance, and process control
Hardware Platforms and Device-Level Limitations
- Survey of prevalent edge hardware (e.g., Raspberry Pi, NVIDIA Jetson, Intel NUC)
- Critical analysis of processing power, memory, and energy constraints
- Selecting the optimal platform based on specific application requirements
Model Development and Optimization for Edge Environments
- Techniques for model compression, pruning, and quantization
- Utilizing TensorFlow Lite and ONNX for embedded execution
- Achieving the right balance between accuracy and speed under resource constraints
Computer Vision and Sensor Fusion at the Edge
- Implementing edge-based visual inspection and continuous monitoring
- Fusing data streams from diverse sensors (vibration, temperature, cameras)
- Performing real-time anomaly detection using Edge Impulse
Communication and Data Exchange Protocols
- Utilizing MQTT for efficient industrial messaging
- Integration with SCADA, OPC-UA, and PLC infrastructure
- Ensuring security and robustness in edge communication layers
Deployment and Field Validation
- Packaging and releasing models onto edge devices
- Performance monitoring and managing software updates
- Case study analysis: executing real-time decision loops with local actuation
Scaling and Maintenance of Edge AI Ecosystems
- Strategic approaches to managing distributed edge devices
- Implementing remote updates and iterative model retraining cycles
- Addressing lifecycle requirements for industrial-grade reliability
Summary and Future Directions
Requirements
- A solid foundation in embedded systems or IoT architectural patterns
- Proficiency in Python or C/C++ programming languages
- Prior exposure to machine learning model creation processes
Target Audience
- Embedded software developers
- Industrial IoT engineering teams
Testimonials (2)
How to use and explore AI for daily works
Giovani Natalia - PT Astra Honda Motor
Course - AI for Supply Chain and Manufacturing Logistics
That we can cover advance topic and work with real-life example
