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Duration 21 hours
Course Outline
Introduction to Edge AI in Industrial Contexts
- The significance of edge computing in manufacturing processes
- Differentiating edge AI from cloud-based solutions
- Practical applications in computer vision, predictive maintenance, and process control
Hardware Platforms and Device-Level Limitations
- Survey of prevalent edge hardware options (Raspberry Pi, NVIDIA Jetson, Intel NUC)
- Critical factors regarding processing power, memory, and energy consumption
- Selecting the optimal platform based on specific application needs
Edge-Oriented Model Development and Optimization
- Techniques for model compression, pruning, and quantization
- Deploying models on embedded systems using TensorFlow Lite and ONNX
- Achieving the right balance between model accuracy and processing speed in resource-constrained environments
Edge-Based Computer Vision and Sensor Fusion
- Implementing visual inspection and monitoring systems at the edge
- Correlating data streams from diverse sensors (vibration, temperature, cameras)
- Achieving real-time anomaly detection utilizing Edge Impulse
Communication Protocols and Data Exchange
- Utilizing MQTT for efficient industrial messaging
- Connecting with SCADA, OPC-UA, and PLC systems
- Ensuring security and robustness in edge-level communications
Deployment Strategies and Field Validation
- Packaging and deploying AI models onto edge devices
- Tracking performance metrics and managing software updates
- Case study analysis: executing real-time decision loops with local actuation
Scaling and Long-Term Maintenance of Edge AI Systems
- Strategies for managing fleets of edge devices
- Implementing remote updates and model retraining cycles
- Addressing lifecycle considerations for industrial-grade stability
Summary and Future Directions
Requirements
- Fundamental knowledge of embedded systems or IoT architectures
- Proficiency in Python or C/C++ programming
- Basic familiarity with the machine learning model development lifecycle
Intended Audience
- Embedded software developers
- Industrial IoT engineering teams
Testimonials (2)
How to use and explore AI for daily works
Giovani Natalia - PT Astra Honda Motor
Course - AI for Supply Chain and Manufacturing Logistics
That we can cover advance topic and work with real-life example
