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Course Outline
Introduction to Edge AI in Industrial Contexts
- The significance of edge computing in manufacturing processes.
- A comparative analysis with cloud-based AI solutions.
- Practical applications in vision systems, predictive maintenance, and control mechanisms.
Hardware Platforms and Device-Level Limitations
- An overview of standard edge hardware, including Raspberry Pi, NVIDIA Jetson, and Intel NUC.
- Evaluating processing power, memory capacity, and energy requirements.
- Choosing the appropriate platform based on specific application demands.
Model Development and Optimization for the Edge
- Techniques for model compression, pruning, and quantization.
- Utilizing TensorFlow Lite and ONNX for embedded deployment strategies.
- Striking a balance between accuracy and speed in resource-constrained environments.
Computer Vision and Sensor Fusion at the Edge
- Implementing edge-based visual inspection and continuous monitoring.
- Merging data streams from diverse sensors, such as vibration, temperature, and cameras.
- Achieving real-time anomaly detection using Edge Impulse.
Communication and Data Exchange Protocols
- Applying MQTT for efficient industrial messaging.
- Integrating with SCADA, OPC-UA, and PLC systems.
- Ensuring security and robustness in edge network communications.
Deployment and Field Validation
- Packaging and deploying AI models onto edge devices.
- Monitoring system performance and managing software updates.
- Case study: Implementing a real-time decision loop with local actuation.
Scaling and Maintaining Edge AI Systems
- Strategies for managing fleets of edge devices.
- Handling remote updates and recurring model retraining cycles.
- Considering lifecycle factors for industrial-grade deployments.
Recap and Recommended Next Steps
Requirements
- A solid grasp of embedded systems or IoT architectural principles.
- Proficiency in programming with Python or C/C++.
- Experience with developing machine learning models.
Target Audience
- Embedded systems developers.
- Industrial IoT technical teams.
21 Hours
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