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Course Outline
Introduction to Edge AI in Industrial Environments
- The significance of edge computing in manufacturing processes
- Comparison against cloud-based AI solutions
- Application scenarios in vision systems, predictive maintenance, and control
Hardware Platforms and Device-Level Limitations
- Overview of standard edge hardware (Raspberry Pi, NVIDIA Jetson, Intel NUC)
- Processing power, memory, and energy efficiency considerations
- Choosing the optimal platform based on application requirements
Model Development and Optimization for Edge
- Techniques for model compression, pruning, and quantization
- Utilizing TensorFlow Lite and ONNX for embedded implementation
- Striking a balance between accuracy and speed in resource-constrained settings
Computer Vision and Sensor Fusion at the Edge
- Edge-based visual inspection and monitoring workflows
- Aggregating data from various sensors (vibration, temperature, cameras)
- Real-time anomaly detection using Edge Impulse
Communication and Data Exchange
- Implementing MQTT for industrial messaging protocols
- Integration with SCADA, OPC-UA, and PLC systems
- Ensuring security and robustness in edge communications
Deployment and Field Testing
- Packaging and deploying models onto edge devices
- Monitoring performance metrics and managing software updates
- Case study: Implementing a real-time decision loop with local actuation
Scaling and Maintenance of Edge AI Systems
- Strategies for managing edge device fleets
- Remote updates and periodic model retraining cycles
- Lifecycle planning for industrial-grade deployment
Summary and Next Steps
Requirements
- Knowledge of embedded systems or IoT frameworks
- Proficiency in Python or C/C++ development
- Basic understanding of machine learning model creation
Target Audience
- Embedded software developers
- Industrial IoT teams
21 Hours
Testimonials (1)
That we can cover advance topic and work with real-life example
