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Duration 21 hours
Course Outline
Fundamentals of Edge AI in Industrial Environments
- The critical role of edge computing in modern manufacturing.
- Contrasting edge solutions with cloud-based AI approaches.
- Applications in machine vision, predictive maintenance, and process control.
Hardware Selection and Device-Level Limitations
- Survey of popular edge hardware platforms (including Raspberry Pi, NVIDIA Jetson, and Intel NUC).
- Managing processing power, memory capacity, and energy efficiency.
- Matching hardware platforms to specific application requirements.
Designing and Optimizing Models for Edge Deployment
- Techniques for model compression, pruning, and quantization.
- Utilizing TensorFlow Lite and ONNX for embedded integration.
- Achieving an optimal balance between accuracy and speed in resource-constrained settings.
Edge-Based Computer Vision and Sensor Fusion
- Implementing visual inspection and monitoring at the edge.
- Fusing data streams from diverse sensors (vibration, temperature, cameras).
- Detecting anomalies in real time using Edge Impulse.
Data Communication and Exchange Mechanisms
- Implementing MQTT for efficient industrial messaging.
- Interfacing with SCADA, OPC-UA, and PLC systems.
- Ensuring security and resilience in edge network communications.
Deployment Strategies and Field Validation
- Packaging and releasing models onto edge devices.
- Performance monitoring and managing software updates.
- Case study: implementing real-time decision loops with local actuation.
Scaling and Maintaining Edge AI Infrastructure
- Strategies for managing distributed edge device fleets.
- Handling remote updates and iterative model retraining.
- Addressing lifecycle considerations for industrial-grade implementations.
Recap and Future Directions
Requirements
- Foundational knowledge of embedded systems or IoT system architecture.
- Proficiency in Python or C/C++ programming.
- Basic understanding of machine learning model creation.
Target Audience
- Embedded software developers.
- Industrial IoT engineering 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
