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Course Outline
Introduction to Edge AI in Industrial Environments
- The significance of edge computing in manufacturing
- Contrasting edge AI with cloud-based solutions
- Applications in vision, predictive maintenance, and process control
Hardware Platforms and Device-Level Limitations
- Survey of standard edge hardware (Raspberry Pi, NVIDIA Jetson, Intel NUC)
- Processing power, memory, and energy constraints
- Choosing the appropriate platform for specific applications
Model Development and Optimisation for Edge
- Techniques for model compression, pruning, and quantization
- Utilising TensorFlow Lite and ONNX for embedded deployment
- Balancing accuracy against speed in resource-constrained settings
Computer Vision and Sensor Fusion at the Edge
- Edge-based visual inspection and surveillance
- Combining data from various sensors (vibration, temperature, cameras)
- Real-time anomaly detection using Edge Impulse
Communication and Data Exchange
- Implementing MQTT for industrial messaging
- Integration with SCADA, OPC-UA, and PLC systems
- Security and robustness in edge communications
Deployment and Field Testing
- Packaging and rolling out models on edge devices
- Tracking performance and handling updates
- Case study: real-time decision loops with local actuation
Scaling and Maintaining Edge AI Systems
- Strategies for managing edge devices
- Remote updates and model retraining cycles
- Lifecycle management for industrial-grade deployment
Summary and Next Steps
Requirements
- Solid knowledge of embedded systems or IoT frameworks
- Proficiency in Python or C/C++ programming
- Working knowledge of machine learning model creation
Target Audience
- Embedded developers
- Industrial IoT teams
21 Hours
Testimonials (1)
That we can cover advance topic and work with real-life example
