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 Edge AI in Industrial Environments
- The significance of edge computing in manufacturing processes
- Differentiation from cloud-based AI approaches
- Practical applications in visual inspection, predictive maintenance, and process control
Hardware Platforms and Device Constraints
- Examination of prevalent edge hardware (Raspberry Pi, NVIDIA Jetson, Intel NUC)
- Evaluation of processing, memory, and power requirements
- Selecting the appropriate platform based on application needs
Model Development and Edge Optimization
- Techniques for model compression, pruning, and quantization
- Utilizing TensorFlow Lite and ONNX for embedded integration
- Achieving a balance between accuracy and speed in resource-limited settings
Computer Vision and Sensor Fusion at the Edge
- Edge-based visual inspection and continuous monitoring
- Combining data streams from various sensors (vibration, temperature, cameras)
- Real-time anomaly detection using Edge Impulse
Communication Protocols and Data Exchange
- Implementing MQTT for industrial message handling
- Integration with SCADA, OPC-UA, and PLC systems
- Ensuring security and robustness in edge network communications
Deployment Strategies and Field Validation
- Packaging and installing models onto edge devices
- Performance monitoring and update management
- Case study: implementing real-time decision loops with local actuation
Scaling and Maintaining Edge AI Ecosystems
- Strategies for managing fleets of edge devices
- Processes for remote updates and iterative model retraining
- Considerations for the full lifecycle of industrial-grade deployments
Recap and Future Directions
Requirements
- Proficiency in embedded systems or IoT architectural concepts
- Coding experience in Python or C/C++
- Knowledge of machine learning model creation
Target Audience
- Embedded software engineers
- Industrial IoT engineering teams
21 Hours
Testimonials (1)
That we can cover advance topic and work with real-life example
