Cursusaanbod

Introduction to Edge AI in Industrial Settings

  • Why edge computing matters in manufacturing
  • Comparison with cloud-based AI
  • Use cases in vision, predictive maintenance, and control

Hardware Platforms and Device-Level Constraints

  • Overview of common edge hardware (Raspberry Pi, NVIDIA Jetson, Intel NUC)
  • Processing, memory, and power considerations
  • Selecting the right platform for application type

Model Development and Optimization for Edge

  • Model compression, pruning, and quantization techniques
  • Using TensorFlow Lite and ONNX for embedded deployment
  • Balancing accuracy vs. speed in constrained environments

Computer Vision and Sensor Fusion at the Edge

  • Edge-based visual inspection and monitoring
  • Integrating data from multiple sensors (vibration, temperature, cameras)
  • Real-time anomaly detection with Edge Impulse

Communication and Data Exchange

  • Using MQTT for industrial messaging
  • Integrating with SCADA, OPC-UA, and PLC systems
  • Security and resilience in edge communications

Deployment and Field Testing

  • Packaging and deploying models on edge devices
  • Monitoring performance and managing updates
  • Case study: real-time decision loop with local actuation

Scaling and Maintenance of Edge AI Systems

  • Edge device management strategies
  • Remote updates and model retraining cycles
  • Lifecycle considerations for industrial-grade deployment

Summary and Next Steps

Vereisten

  • An understanding of embedded systems or IoT architectures
  • Experience with Python or C/C++ programming
  • Familiarity with machine learning model development

Audience

  • Embedded developers
  • Industrial IoT teams
 21 Uren

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