Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to Industrial Edge AI
- The significance of edge computing in manufacturing operations
- Contrasting edge AI with cloud-based solutions
- Applications in visual inspection, predictive maintenance, and control systems
Hardware Platforms and On-Device Limitations
- Survey of prevalent edge hardware options (Raspberry Pi, NVIDIA Jetson, Intel NUC)
- Evaluating processing power, memory capacity, and energy usage
- Choosing the appropriate platform based on specific application needs
Developing and Optimizing Models for Edge Deployment
- Techniques for model compression, pruning, and quantization
- Utilizing TensorFlow Lite and ONNX for embedded integration
- Weighing accuracy against speed in resource-constrained settings
Edge-Based Computer Vision and Sensor Integration
- Visual inspection and monitoring tasks executed on the edge
- Aggregating data from diverse sensors (vibration, temperature, cameras)
- Detecting real-time anomalies using Edge Impulse
Data Communication and Exchange
- Implementing MQTT for industrial messaging
- Connecting with SCADA, OPC-UA, and PLC environments
- Ensuring security and robustness in edge network communications
Deployment and Field Verification
- Preparing and installing models on edge devices
- Tracking performance metrics and handling software updates
- Case study: Executing real-time decision loops with local actuation
Scaling and Sustaining Edge AI Systems
- Strategies for managing edge device fleets
- Managing remote updates and periodic model retraining
- Addressing lifecycle aspects for industrial-grade deployments
Conclusion and Future Pathways
Requirements
- Solid grasp of embedded systems or IoT system designs
- Proficiency in Python or C/C++ development
- Knowledge of machine learning model creation
Target Audience
- Embedded software developers
- Industrial IoT engineering teams
21 Hours
Testimonials (1)
That we can cover advance topic and work with real-life example
