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Course Outline
Introduction to Edge AI in Industrial Environments
- The critical role of edge computing in manufacturing workflows
- A comparative analysis of edge versus cloud-based AI
- Practical applications in computer vision, predictive maintenance, and process control
Hardware Platforms and Device-Level Limitations
- An overview of prevalent edge hardware options (e.g., Raspberry Pi, NVIDIA Jetson, Intel NUC)
- Key factors regarding processing power, memory capacity, and energy consumption
- Criteria for 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 implementation
- Achieving the right balance between model accuracy and processing speed in resource-constrained settings
Computer Vision and Sensor Fusion at the Edge
- Implementing edge-based visual inspection and continuous monitoring
- Aggregating data from diverse sensors (vibration, temperature, cameras)
- Performing real-time anomaly detection using Edge Impulse
Communication and Data Exchange Mechanisms
- Implementing MQTT for efficient industrial messaging
- Integration strategies for SCADA, OPC-UA, and PLC systems
- Ensuring security and resilience in edge network communications
Deployment and Field Validation
- Packaging models and deploying them onto edge devices
- Strategies for performance monitoring and managing system updates
- Case study: Implementing a real-time decision loop with local actuation
Scaling and Maintaining Edge AI Systems
- Effective strategies for managing distributed edge devices
- Handling remote updates and establishing model retraining cycles
- Long-term lifecycle considerations for industrial-grade deployments
Course Summary and Recommended Next Steps
Requirements
- Foundational knowledge of embedded systems or IoT architectures
- Programming experience in Python or C/C++
- Basic familiarity with developing machine learning models
Audience
- Embedded developers
- Industrial IoT teams
21 Hours
Testimonials (1)
That we can cover advance topic and work with real-life example
