Edge AI for Manufacturing: Real-Time Intelligence at the Device Level Training Course
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
Runs with a minimum of 4 + people. For 1-to-1 or private group training, request a quote.
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