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Course Outline
Intro to Edge AI in Industrial Contexts
- The significance of edge computing in production
- Contrasting with cloud-based AI solutions
- Applications in vision, predictive maintenance, and control systems
Hardware Platforms and Device-Level Limitations
- Overview of typical edge hardware (Raspberry Pi, NVIDIA Jetson, Intel NUC)
- Factors regarding processing power, memory, and energy consumption
- Choosing the appropriate platform for specific application needs
Model Creation and Optimization for Edge Environments
- Techniques for model compression, pruning, and quantization
- Utilizing TensorFlow Lite and ONNX for embedded implementation
- Striking a balance between accuracy and speed in resource-constrained settings
Computer Vision and Sensor Integration at the Edge
- Edge-based visual inspection and oversight
- Combining data from diverse sensors (vibration, temperature, cameras)
- Instant anomaly detection using Edge Impulse
Connectivity and Data Transfer
- Applying MQTT for industrial communication
- Interfacing with SCADA, OPC-UA, and PLC systems
- Ensuring security and resilience in edge networks
Deployment and On-Site Testing
- Packaging and rolling out models onto edge hardware
- Tracking performance and overseeing updates
- Case study: real-time decision cycles involving local actuation
Scaling and Upkeeping Edge AI Systems
- Strategies for managing edge devices
- Remote upgrades and model retraining processes
- Lifecycle considerations for industrial-grade operations
Recap and Future Directions
Requirements
- Knowledge of embedded systems or IoT architectures
- Proficiency in Python or C/C++ development
- Experience in creating machine learning models
Target Audience
- Embedded software developers
- Industrial IoT teams
21 Hours
Testimonials (1)
That we can cover advance topic and work with real-life example
