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Course Outline
Introduction to Edge AI in Industrial Environments
- The significance of edge computing in manufacturing
- Contrast with cloud-based AI solutions
- Applications in computer vision, predictive maintenance, and control systems
Hardware Platforms and Device Constraints
- Overview of popular 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 Deployment
- Techniques for model compression, pruning, and quantization
- Utilizing TensorFlow Lite and ONNX for embedded deployment
- Balancing accuracy against speed in resource-limited settings
Computer Vision and Sensor Fusion at the Edge
- Edge-based visual inspection and monitoring systems
- Consolidating data from various sensors (vibration, temperature, cameras)
- 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 resilience in edge communications
Deployment and Field Testing Procedures
- Packaging and deploying models onto edge devices
- Performance monitoring and update management
- Case study: real-time decision loops with local actuation
Scaling and Maintaining Edge AI Systems
- Strategies for managing edge devices at scale
- Remote updates and model retraining cycles
- Lifecycle considerations for industrial-grade deployments
Summary and Recommended Next Steps
Requirements
- Basic knowledge of embedded systems or IoT architectures
- Proficiency in Python or C/C++ programming
- Familiarity with creating machine learning models
Target Audience
- Embedded developers
- Industrial IoT teams
21 Hours
Testimonials (1)
That we can cover advance topic and work with real-life example
