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Course Outline
Introduction to Edge AI in Industrial Automation
- Overview of Edge AI and its industrial applications.
- Benefits and challenges of Edge AI in industrial settings.
- Case studies of successful Edge AI applications in manufacturing.
Setting Up the Edge AI Environment
- Installing and configuring Edge AI tools.
- Setting up industrial sensors and data collection systems.
- Introduction to relevant Edge AI frameworks and libraries.
- Hands-on exercises for environment setup.
Predictive Maintenance with Edge AI
- Introduction to predictive maintenance.
- Developing AI models for equipment health monitoring.
- Implementing real-time fault detection and prediction.
- Hands-on exercises for predictive maintenance.
Quality Control Using Edge AI
- Overview of quality control in manufacturing.
- AI techniques for defect detection and classification.
- Implementing vision-based quality control systems.
- Hands-on exercises for quality control applications.
Process Optimization with Edge AI
- Introduction to process optimization.
- Using AI for real-time process monitoring and control.
- Implementing AI-driven decision-making systems.
- Hands-on exercises for process optimization.
Deploying and Managing Edge AI Solutions
- Deploying AI models on industrial edge devices.
- Monitoring and maintaining Edge AI systems.
- Troubleshooting and optimizing deployed models.
- Hands-on exercises for deployment and management.
Tools and Frameworks for Industrial Edge AI
- Overview of tools and frameworks (e.g., TensorFlow Lite, OpenVINO).
- Using TensorFlow Lite for industrial AI applications.
- Hands-on exercises with optimization tools.
Real-World Applications and Case Studies
- Review of successful industrial Edge AI projects.
- Discussion of industry-specific use cases.
- Hands-on project for building and optimizing a real-world industrial AI application.
Summary and Next Steps
Requirements
- An understanding of AI and machine learning concepts.
- Experience with industrial automation systems.
- Basic programming skills (Python recommended).
Target Audience
- Industrial engineers.
- Manufacturing professionals.
- AI developers.
14 Hours
Testimonials (1)
That we can cover advance topic and work with real-life example
