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Course Outline
Introduction to Edge AI in Industrial Contexts
- The significance of edge computing in manufacturing processes
- Comparative analysis with cloud-based AI solutions
- Practical applications in vision, predictive maintenance, and control systems
Hardware Platforms and Device-Level Constraints
- Overview of prevalent edge hardware (Raspberry Pi, NVIDIA Jetson, Intel NUC)
- Considerations for processing power, memory, and energy consumption
- Selecting the appropriate platform based on application requirements
Model Development and Optimization for Edge
- Techniques for model compression, pruning, and quantization
- Implementing embedded deployments using TensorFlow Lite and ONNX
- Balancing accuracy against speed in resource-constrained environments
Computer Vision and Sensor Fusion at the Edge
- Edge-based visual inspection and continuous monitoring
- Fusing data from diverse sensors (vibration, temperature, cameras)
- Executing real-time anomaly detection using Edge Impulse
Communication and Data Exchange
- Leveraging MQTT for industrial messaging protocols
- Integration with SCADA, OPC-UA, and PLC systems
- Ensuring security and resilience in edge communications
Deployment and Field Testing
- Packaging and deploying models onto edge devices
- Monitoring performance metrics and managing software updates
- Case study: Implementing a real-time decision loop with local actuation
Scaling and Maintenance of Edge AI Systems
- Strategies for managing large-scale edge device fleets
- Remote updates and model retraining cycles
- Lifecycle considerations for industrial-grade deployments
Summary and Next Steps
Requirements
- Proficiency in embedded systems or IoT architectures
- Practical experience with Python or C/C++ programming
- Basic knowledge of machine learning model development
Target Audience
- Embedded software developers
- Industrial IoT teams
21 Hours
Testimonials (1)
That we can cover advance topic and work with real-life example
