Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to Edge AI in Industrial Contexts
- The significance of edge computing in manufacturing
- Contrast with cloud-based AI solutions
- Applications in vision, predictive maintenance, and control
Hardware Platforms and Device-Level Limitations
- Overview of standard edge hardware (Raspberry Pi, NVIDIA Jetson, Intel NUC)
- Considerations for processing power, memory, and energy consumption
- Choosing the appropriate platform for specific applications
Model Development and Optimization for Edge
- Techniques for model compression, pruning, and quantization
- Utilizing TensorFlow Lite and ONNX for embedded deployment
- Balancing accuracy and speed in resource-constrained settings
Computer Vision and Sensor Fusion at the Edge
- Edge-based visual inspection and monitoring systems
- Combining data from multiple sensors (vibration, temperature, cameras)
- Real-time anomaly detection using Edge Impulse
Communication and Data Exchange
- Implementing MQTT for industrial messaging
- Integration with SCADA, OPC-UA, and PLC systems
- Security and resilience in edge communication
Deployment and Field Testing
- Packaging and deploying models on edge devices
- Performance monitoring and update management
- Case study: Real-time decision loops with local actuation
Scaling and Maintenance of Edge AI Systems
- Strategies for managing edge devices
- Remote updates and model retraining cycles
- Lifecycle considerations for industrial-grade deployment
Summary and Next Steps
Requirements
- Knowledge of embedded systems or IoT architectures
- Proficiency in Python or C/C++ programming
- Experience with machine learning model creation
Audience
- Embedded developers
- Industrial IoT teams
21 Hours
Testimonials (1)
That we can cover advance topic and work with real-life example
