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Course Outline
Edge AI Foundations in Industrial Contexts
- The significance of edge computing in manufacturing processes
- Contrasting edge AI with cloud-based solutions
- Applications in visual inspection, predictive maintenance, and process control
Hardware Platforms and Device Constraints
- Overview of prevalent edge hardware options (Raspberry Pi, NVIDIA Jetson, Intel NUC)
- Factors regarding processing power, memory capacity, and energy consumption
- Choosing the optimal platform based on specific application requirements
Edge-Oriented Model Development and Optimization
- Techniques for model compression, pruning, and quantization
- Implementing TensorFlow Lite and ONNX for embedded environments
- Achieving a balance between accuracy and speed in resource-limited settings
Computer Vision and Sensor Fusion at the Edge
- Conducting visual inspection and monitoring via edge computing
- Consolidating data from diverse sensors (vibration, temperature, cameras)
- Performing real-time anomaly detection using Edge Impulse
Communication and Data Interchange
- Utilizing MQTT for industrial message transmission
- Integration with SCADA, OPC-UA, and PLC systems
- Ensuring security and robustness in edge network communications
Deployment and Field Validation
- Packaging and releasing models onto edge devices
- Tracking performance metrics 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 device fleets
- Implementing remote updates and periodic model retraining
- Considerations for industrial-grade lifecycle management
Recap and Future Directions
Requirements
- Knowledge of embedded systems or IoT architectural frameworks
- Proficiency in Python or C/C++ programming
- Experience with developing machine learning models
Target Audience
- Embedded software developers
- Industrial IoT teams
21 Hours
Testimonials (1)
That we can cover advance topic and work with real-life example
