Course Outline

Introduction to Edge AI in Industrial Settings

  • The significance of edge computing in manufacturing operations
  • Comparison with cloud-based artificial intelligence solutions
  • Applications in vision systems, predictive maintenance, and control mechanisms

Hardware Platforms and Device-Level Constraints

  • Overview of common edge hardware platforms (Raspberry Pi, NVIDIA Jetson, Intel NUC)
  • Considerations for processing power, memory capacity, and energy consumption
  • Selecting the appropriate platform based on application requirements

Model Development and Optimization for Edge Deployment

  • Techniques for model compression, pruning, and quantization
  • Utilizing TensorFlow Lite and ONNX for embedded system deployment
  • Balancing accuracy with performance in resource-constrained environments

Computer Vision and Sensor Fusion at the Edge

  • Implementing edge-based visual inspection and monitoring systems
  • Integrating data from various sensors (vibration, temperature, cameras)
  • Real-time anomaly detection using Edge Impulse

Communication and Data Exchange for Government Operations

  • Utilizing MQTT for industrial messaging protocols
  • Integrating with SCADA, OPC-UA, and PLC systems in industrial environments
  • Ensuring security and resilience in edge communication networks

Deployment and Field Testing for Government Applications

  • Packaging and deploying models on edge devices for government use
  • Monitoring performance and managing software updates
  • Case study: implementing a real-time decision loop with local actuation

Scaling and Maintenance of Edge AI Systems for Government Operations

  • Strategies for managing edge devices in government deployments
  • Implementing remote updates and model retraining cycles
  • Lifecycle considerations for industrial-grade deployment in government settings

Summary and Next Steps

Requirements

  • Knowledge of embedded systems or Internet of Things (IoT) architectures
  • Experience with Python or C/C++ programming languages
  • Familiarity with the development of machine learning models

Audience for Government

  • Embedded systems developers
  • Industrial IoT teams
 21 Hours

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