Edge AI for Manufacturing: Real-Time Intelligence at the Device Level

• An understanding of embedded systems or IoT architectures
• Experience with Python or C/C++ programming
• Familiarity with machine learning model development

Audience

• Embedded developers
• Industrial IoT teams

Edge AI is the deployment of artificial intelligence models directly on devices and machines at the edge of the network, enabling real-time decision-making with minimal latency.

This instructor-led, live training (online or onsite) is aimed at advanced-level embedded and IoT professionals who wish to deploy AI-powered logic and control systems in manufacturing environments where speed, reliability, and offline operation are critical.

By the end of this training, participants will be able to:

• Understand the architecture and benefits of edge AI systems.
• Build and optimize AI models for deployment on embedded devices.
• Use tools like TensorFlow Lite and OpenVINO for low-latency inference.
• Integrate edge intelligence with sensors, actuators, and industrial protocols.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to Edge AI in Industrial Settings

• Why edge computing matters in manufacturing
• Comparison with cloud-based AI
• Use cases in vision, predictive maintenance, and control

Hardware Platforms and Device-Level Constraints

• Overview of common edge hardware (Raspberry Pi, NVIDIA Jetson, Intel NUC)
• Processing, memory, and power considerations
• Selecting the right platform for application type

Model Development and Optimization for Edge

• Model compression, pruning, and quantization techniques
• Using TensorFlow Lite and ONNX for embedded deployment
• Balancing accuracy vs. speed in constrained environments

Computer Vision and Sensor Fusion at the Edge

• Edge-based visual inspection and monitoring
• Integrating data from multiple sensors (vibration, temperature, cameras)
• Real-time anomaly detection with Edge Impulse

Communication and Data Exchange

• Using MQTT for industrial messaging
• Integrating with SCADA, OPC-UA, and PLC systems
• Security and resilience in edge communications

Deployment and Field Testing

• Packaging and deploying models on edge devices
• Monitoring performance and managing updates
• Case study: real-time decision loop with local actuation

Scaling and Maintenance of Edge AI Systems

• Edge device management strategies
• Remote updates and model retraining cycles
• Lifecycle considerations for industrial-grade deployment

Summary and Next Steps