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Course Outline
Introduction to Edge AI in Industrial Settings
- Why edge computing is vital in manufacturing
- Comparison with cloud-based AI approaches
- Practical use cases in visual inspection, predictive maintenance, and process control
Hardware Platforms and Device-Level Constraints
- Overview of common edge hardware platforms (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 Optimisation for Edge Deployment
- Techniques for model compression, pruning, and quantisation
- Using TensorFlow Lite and ONNX for embedded deployment
- Balancing accuracy and speed in resource-constrained environments
Computer Vision and Sensor Fusion at the Edge
- Edge-based visual inspection and continuous monitoring
- Integrating data from multiple sensors (vibration, temperature, cameras)
- Real-time anomaly detection using Edge Impulse
Communication and Data Exchange
- Leveraging MQTT for industrial messaging
- Integration with SCADA, OPC-UA, and PLC systems
- Ensuring security and resilience in edge communications
Deployment and Field Testing
- Packaging and deploying models on edge devices
- Monitoring performance and managing updates effectively
- Case study: real-time decision loop with local actuation
Scaling and Maintenance of Edge AI Systems
- Strategies for managing edge devices at scale
- Remote updates and model retraining cycles
- Lifecycle considerations for industrial-grade deployments
Summary and Next Steps
Requirements
- A solid understanding of embedded systems or IoT architectures
- Practical experience with Python or C/C++ programming
- Familiarity with machine learning model development
Target Audience
- Embedded systems developers
- Industrial IoT teams
21 Hours
Testimonials (1)
That we can cover advance topic and work with real-life example
