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Course Outline
Introduction to Edge AI in Industrial Settings
- The importance of edge computing in manufacturing.
- Comparison with cloud-based AI.
- Use cases in vision, predictive maintenance, and control.
Hardware Platforms and Device-Level Constraints
- Overview of common edge hardware (Raspberry Pi, NVIDIA Jetson, Intel NUC).
- Considerations for processing, memory, and power.
- Selecting the appropriate platform for the specific application.
Model Development and Optimization for Edge
- Techniques for model compression, pruning, and quantization.
- Using TensorFlow Lite and ONNX for embedded deployment.
- Balancing accuracy versus speed in constrained environments.
Computer Vision and Sensor Fusion at the Edge
- Edge-based visual inspection and monitoring.
- Integrating data from multiple sensors (vibration, temperature, cameras).
- Real-time anomaly detection with Edge Impulse.
Communication and Data Exchange
- Using MQTT for industrial messaging.
- Integration with SCADA, OPC-UA, and PLC systems.
- Security and resilience in edge communications.
Deployment and Field Testing
- Packaging and deploying models on edge devices.
- Monitoring performance and managing updates.
- Case study: real-time decision loop with local actuation.
Scaling and Maintenance of Edge AI Systems
- Edge device management strategies.
- Remote updates and model retraining cycles.
- Lifecycle considerations for industrial-grade deployment.
Summary and Next Steps
Requirements
- A foundational understanding of embedded systems or IoT architectures.
- Experience with Python or C/C++ programming.
- Familiarity with machine learning model development.
Audience
- Embedded developers.
- Industrial IoT teams.
21 Hours
Testimonials (1)
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