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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 control systems
Hardware Platforms and Device Constraints
- Key Edge hardware platforms (Raspberry Pi, NVIDIA Jetson, Intel NUC)
- Considerations regarding processing power, memory, and energy consumption
- Choosing the optimal platform based on specific application requirements
Model Development and Optimization for Edge
- Techniques for model compression, pruning, and quantization
- Utilizing TensorFlow Lite and ONNX for embedded implementations
- Achieving the right balance between accuracy and speed in resource-constrained environments
Computer Vision and Sensor Fusion on Edge Devices
- Performing visual inspection and monitoring via Edge AI
- Fusing data streams from various sensors (vibration, temperature, cameras)
- Implementing real-time anomaly detection using Edge Impulse
Communication and Data Interchange
- Employing MQTT for industrial messaging protocols
- 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 hardware
- Monitoring performance metrics and managing system updates
- Case study: implementing a real-time decision loop with local actuation
Scaling and Maintaining Edge AI Systems
- Strategies for managing fleets of Edge devices
- Managing remote updates and model retraining cycles
- Lifecycle management for industrial-grade deployments
Summary and Future Directions
Requirements
- Solid knowledge of embedded systems or IoT architectures
- Proficiency in Python or C/C++ programming
- Practical experience with machine learning model creation
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
