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.
Duration 21 hours
Course Outline
Introduction to Edge AI in Industrial Environments
- The importance of edge computing in manufacturing
- Comparing edge AI with cloud-based AI
- Use cases in visual inspection, predictive maintenance, and control
Hardware Platforms and Device-Level Limitations
- Overview of common edge hardware (Raspberry Pi, NVIDIA Jetson, Intel NUC)
- Considerations for processing power, memory, and energy consumption
- Selecting the appropriate platform for specific application types
Model Development and Optimization for the Edge
- Techniques for model compression, pruning, and quantization
- Deploying models on embedded systems using TensorFlow Lite and ONNX
- Balancing accuracy against speed in resource-constrained environments
Computer Vision and Sensor Fusion at the Edge
- Edge-based visual inspection and monitoring workflows
- Combining data from multiple sensors (vibration, temperature, cameras)
- Real-time anomaly detection using Edge Impulse
Communication and Data Exchange
- Utilizing MQTT for industrial messaging
- Integration with SCADA, OPC-UA, and PLC systems
- Ensuring security and resilience in edge communications
Deployment and Field Testing
- Packaging and deploying models onto edge devices
- Monitoring performance and managing software updates
- Case study: Implementing a real-time decision loop with local actuation
Scaling and Maintaining Edge AI Systems
- Strategies for managing edge devices at scale
- Handling remote updates and model retraining cycles
- Lifecycle considerations for industrial-grade deployment
Summary and Next Steps
Requirements
- A solid understanding of embedded systems or IoT architectures
- Practical experience with Python or C/C++ programming
- Familiarity with developing machine learning models
Target Audience
- Embedded developers
- Industrial IoT teams
Testimonials (2)
How to use and explore AI for daily works
Giovani Natalia - PT Astra Honda Motor
Course - AI for Supply Chain and Manufacturing Logistics
That we can cover advance topic and work with real-life example
