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
Intro to Edge AI in Industrial Contexts
- The importance of edge computing in manufacturing workflows
- Edge AI versus cloud-based alternatives
- Applications in machine vision, predictive maintenance, and process control
Hardware Platforms and Device-Level Limitations
- Review of standard edge hardware (Raspberry Pi, NVIDIA Jetson, Intel NUC)
- Factors in processing power, memory capacity, and energy consumption
- Choosing appropriate platforms based on specific application needs
Developing and Optimizing Models for Edge
- Techniques for model compression, pruning, and quantization
- Deploying models on embedded systems using TensorFlow Lite and ONNX
- Managing the trade-off between accuracy and speed in resource-constrained settings
Edge-Based Computer Vision and Sensor Fusion
- Implementing visual inspection and monitoring at the edge
- Aggregating data from diverse sensors (vibration, thermal, optical)
- Performing real-time anomaly detection using Edge Impulse
Communication and Data Interchange
- Utilizing MQTT for industrial message passing
- Connectivity with SCADA, OPC-UA, and PLC systems
- Ensuring security and robustness in edge network communications
Deployment and On-Site Validation
- Preparing and installing models onto edge hardware
- Tracking performance metrics and handling software updates
- Case study: Executing a real-time decision loop with local actuation
Scaling and Sustaining Edge AI Systems
- Strategies for managing fleets of edge devices
- Implementing remote updates and iterative model retraining
- Addressing lifecycle requirements for industrial-grade deployments
Recap and Future Directions
Requirements
- Foundational knowledge of embedded systems or IoT architectures
- Proficiency in Python or C/C++ programming
- Experience with developing machine learning models
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
