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Course Outline
Introduction to Edge AI in Industrial Contexts
- The significance of edge computing in manufacturing operations
- Contrasting edge solutions with cloud-based AI
- Applications in computer vision, predictive maintenance, and process control
Hardware Platforms and Device-Level Limitations
- Survey of popular edge hardware (Raspberry Pi, NVIDIA Jetson, Intel NUC)
- Factors regarding processing power, memory, and energy consumption
- Choosing the appropriate platform based on specific application needs
Model Development and Optimization for Edge Environments
- Techniques for model compression, pruning, and quantization
- Utilizing TensorFlow Lite and ONNX for embedded deployment
- Striking a balance between accuracy and speed in resource-constrained settings
Computer Vision and Sensor Fusion at the Edge
- Visual inspection and monitoring capabilities at the edge
- Consolidating data from various sensors (vibration, temperature, cameras)
- Real-time anomaly detection utilizing Edge Impulse
Communication and Data Interchange
- Implementing MQTT for industrial messaging
- Integration with SCADA, OPC-UA, and PLC systems
- Ensuring security and resilience in edge communication networks
Deployment and Field Validation
- Packaging and deploying models onto edge devices
- Performance monitoring and update management
- Case study: executing real-time decision loops with local actuation
Scaling and Maintaining Edge AI Systems
- Strategies for managing edge devices
- Managing remote updates and model retraining cycles
- Considering lifecycle factors for industrial-grade deployments
Conclusion and Future Directions
Requirements
- A solid grasp of embedded systems or IoT architectures
- Practical experience with Python or C/C++ programming
- Familiarity with the development of machine learning models
Target Audience
- Embedded software developers
- Industrial IoT engineering teams
21 Hours
Testimonials (1)
That we can cover advance topic and work with real-life example
