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Course Outline
Foundamentals of Edge AI in Industrial Contexts
- The significance of edge computing in manufacturing workflows.
- Practical applications in machine vision, predictive maintenance, and process control.
Hardware Platforms and Device-Level Limitations
- Survey of prevalent edge hardware options (Raspberry Pi, NVIDIA Jetson, Intel NUC).
- Key considerations for processing power, memory, and energy efficiency.
- Selecting the optimal platform based on specific application needs.
Edge-Focused Model Development and Optimization
- Techniques for model compression, pruning, and quantization.
- Utilizing TensorFlow Lite and ONNX for embedded system integration.
Computer Vision and Sensor Fusion at the Edge
- Implementing edge-based visual inspection and continuous monitoring.
- Aggregating data from diverse sensors (vibration, temperature, cameras).
- Real-time anomaly detection using Edge Impulse.
Communication Protocols and Data Exchange
- Employing MQTT for robust industrial messaging.
- Interfacing with SCADA, OPC-UA, and PLC systems.
Deployment and Field Validation
- Packaging and deploying optimized models onto edge devices.
- Performance monitoring and managing software updates.
- Case study: Implementing real-time decision loops with local actuation.
Scaling and Maintaining Edge AI Ecosystems
- Strategies for managing large-scale edge device fleets.
- Executing remote updates and establishing model retraining cycles.
- Lifecycle planning for industrial-grade deployments.
Conclusion and Future Directions
Requirements
- Solid grasp of embedded systems or IoT architectural principles.
- Proficiency in Python or C/C++ programming languages.
- Established knowledge of machine learning model development processes.
Target Audience
- Embedded system developers.
- Industrial IoT engineering teams.
21 Hours
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