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Course Outline
Introduction to Edge AI in Industrial Environments
- The significance of edge computing in manufacturing
- Contrasting with cloud-based AI solutions
- Applications in visual inspection, predictive maintenance, and process control
Hardware Platforms and Device-Level Limitations
- Review of prevalent edge hardware (Raspberry Pi, NVIDIA Jetson, Intel NUC)
- Considerations for processing power, memory, and energy consumption
- Choosing the appropriate platform based on application requirements
Model Development and Optimization for the Edge
- Techniques for model compression, pruning, and quantization
- Utilizing TensorFlow Lite and ONNX for embedded deployments
- Balancing accuracy against speed in resource-constrained settings
Computer Vision and Sensor Fusion at the Edge
- Implementing edge-based visual inspection and monitoring
- Fusing data from various sensors (vibration, temperature, cameras)
- Executing real-time anomaly detection using Edge Impulse
Communication and Data Exchange Mechanisms
- Employing MQTT for industrial messaging
- Integration with SCADA, OPC-UA, and PLC systems
- Ensuring security and robustness in edge communication
Deployment and Field Validation
- Packaging and deploying models onto edge devices
- Tracking performance and managing software updates
- Case study: Implementing a real-time decision loop with local actuation
Scaling and Maintaining Edge AI Systems
- Strategies for managing edge devices
- Handling remote updates and model retraining cycles
- Addressing lifecycle aspects for industrial-grade deployment
Recap and Future Directions
Requirements
- A foundational understanding of embedded systems or IoT architectures
- Proficiency in Python or C/C++ programming
- Experience with developing 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
