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Course Outline
Introduction to Edge AI in Industrial Environments
- The significance of edge computing in manufacturing contexts.
- Differentiating edge solutions from cloud-based AI.
- Key applications in visual inspection, predictive maintenance, and automated control.
Hardware Platforms and Device-Level Limitations
- Survey of popular edge hardware options (e.g., Raspberry Pi, NVIDIA Jetson, Intel NUC).
- Critical factors in processing power, memory, and energy consumption.
- Selecting the optimal platform for specific application requirements.
Model Development and Optimization for Edge Deployment
- Techniques for model compression, pruning, and quantization.
- Utilizing TensorFlow Lite and ONNX for embedded implementations.
- Navigating the trade-off between accuracy and speed in resource-constrained settings.
Computer Vision and Sensor Fusion at the Edge
- Edge-based visual inspection and continuous monitoring.
- Aggregating data from diverse sensors (vibration, temperature, cameras).
- Implementing real-time anomaly detection using Edge Impulse.
Communication and Data Exchange Mechanisms
- Applying MQTT for efficient industrial messaging.
- Integration with SCADA, OPC-UA, and PLC systems.
- Ensuring security and resilience in edge communication networks.
Deployment and Field Validation
- Packaging models and deploying them onto edge devices.
- Monitoring system performance and managing software updates.
- Case study analysis: executing a real-time decision loop with local actuation.
Scaling and Maintaining Edge AI Systems
- Strategies for managing distributed edge devices.
- Handling remote updates and cyclical model retraining.
- Addressing lifecycle considerations for industrial-grade deployments.
Conclusion and Future Directions
Requirements
- Familiarity with embedded systems or IoT architectural principles.
- Proficiency in Python or C/C++ programming languages.
- Basic knowledge of machine learning model development.
Target Audience
- Embedded software developers.
- Industrial IoT engineering teams.
21 Hours
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