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Course Outline
Foundations of Edge AI in Industrial Applications
- The significance of edge computing in production environments
- Practical applications in computer vision, predictive upkeep, and process control
Device Hardware and Operational Limitations
- Review of standard edge platforms (Raspberry Pi, NVIDIA Jetson, Intel NUC)
- Matching hardware specifications to specific application needs
Edge-Specific Model Creation and Refinement
- Optimizing the trade-off between accuracy and processing speed in resource-limited settings
Visual Analysis and Multi-Sensor Integration at the Edge
- Implementing visual inspection and oversight via edge devices
- Correlating inputs from diverse sources including vibration, thermal, and optical sensors
- Detecting anomalies in real-time using Edge Impulse
Network Communication and Data Handling
- Utilizing MQTT for industrial data transmission
- Connecting with SCADA, OPC-UA, and PLC infrastructure
- Ensuring robustness and security in edge network exchanges
Live Deployment and Operational Verification
- Preparing and installing models onto edge hardware
- Tracking system performance and managing software updates
- Case analysis: closed-loop decision making with immediate local actuation
Expanding and Sustaining Edge AI Infrastructures
- Strategies for managing distributed edge devices
- Remote patching and ongoing model retraining workflows
- Addressing full lifecycle requirements for industrial-grade operations
Key Takeaways and Future Directions
Requirements
- Proficiency in embedded systems or IoT system architectures
- Practical experience coding in Python or C/C++
- Working knowledge of machine learning model creation
Target Participants
- Embedded software engineers
- Industrial IoT engineering teams
21 Hours
Testimonials (1)
That we can cover advance topic and work with real-life example
