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Course Outline
Fundamentals of Edge AI in Industrial Environments
- The significance of edge computing in manufacturing processes
- Contrasting edge solutions with cloud-based AI
- Applications in vision systems, predictive maintenance, and process control
Hardware Platforms and Device-Level Limitations
- Overview of prevalent edge hardware (Raspberry Pi, NVIDIA Jetson, Intel NUC)
- Key factors in processing power, memory, and energy consumption
- Choosing the appropriate platform based on application requirements
Developing and Optimizing Models for the Edge
- Techniques for model compression, pruning, and quantization
- Utilizing TensorFlow Lite and ONNX for embedded deployment
- Striking a balance between accuracy and speed in resource-constrained settings
Computer Vision and Sensor Fusion at the Edge
- Implementing edge-based visual inspection and continuous monitoring
- Merging data from various sensors (vibration, temperature, cameras)
- Real-time anomaly detection using Edge Impulse
Communication and Data Exchange
- Employing MQTT for industrial messaging
- Integration with SCADA, OPC-UA, and PLC systems
- Ensuring security and resilience in edge network communications
Deployment and Field Testing
- Packaging and rolling out models onto edge devices
- Performance monitoring and managing system updates
- Case study: achieving real-time decision loops with local actuation
Scaling and Maintaining Edge AI Systems
- Strategies for managing fleets of edge devices
- Remote updates and continuous model retraining cycles
- Lifecycle considerations for industrial-grade deployments
Conclusion and Future Steps
Requirements
- A solid grasp of embedded systems or IoT architectures
- Practical experience programming in Python or C/C++
- Proficiency in 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
