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Duration 21 hours
Course Outline
Introduction to Edge AI in Industrial Contexts
- The significance of edge computing in manufacturing workflows
- Contrasting edge solutions with cloud-based AI architectures
- Practical applications in machine vision, predictive maintenance, and control systems
Hardware Platforms and Device-Level Limitations
- Survey of prevalent edge hardware (Raspberry Pi, NVIDIA Jetson, Intel NUC)
- Critical factors regarding processing power, memory, and energy consumption
- Strategically selecting the optimal platform for specific application requirements
Model Development and Edge Optimization
- Techniques for model compression, pruning, and quantization
- Employing TensorFlow Lite and ONNX for embedded deployment
- Balancing model accuracy against inference speed in resource-constrained environments
Computer Vision and Sensor Fusion on the Edge
- Implementing edge-based visual inspection and continuous monitoring
- Fusing data streams from diverse sensors (vibration, temperature, cameras)
- Achieving real-time anomaly detection utilizing Edge Impulse
Communication and Data Interoperability
- Implementing MQTT for industrial messaging
- Interfacing with SCADA, OPC-UA, and PLC systems
- Ensuring security and resilience in edge network communications
Deployment and Field Validation
- Packaging and deploying models onto edge devices
- Performance monitoring and managing version updates
- Case study: Executing a real-time decision loop with local actuation
Scaling and Sustaining Edge AI Systems
- Strategies for managing fleets of edge devices
- Facilitating remote updates and establishing model retraining cycles
- Considering lifecycle factors for industrial-grade deployments
Recap and Future Pathways
Requirements
- Foundational knowledge of embedded systems or IoT architectures
- Programming proficiency in Python or C/C++
- Familiarity with the development process for machine learning models
Target Audience
- Embedded systems developers
- Industrial IoT 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
