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.
Duration 14 hours
Course Outline
Introduction to Edge AI in Industrial Automation
- Exploring Edge AI and its industrial applications
- Analyzing the advantages and challenges of Edge AI in industrial contexts
- Examining case studies of successful Edge AI implementations in manufacturing
Establishing the Edge AI Environment
- Installation and configuration of Edge AI tools
- Setting up industrial sensors and data acquisition systems
- Introduction to key Edge AI frameworks and libraries
- Practical exercises for environment configuration
Predictive Maintenance via Edge AI
- Fundamentals of predictive maintenance
- Creating AI models for monitoring equipment health
- Enabling real-time fault detection and prediction
- Hands-on practical tasks for predictive maintenance
Quality Control with Edge AI
- Overview of quality control in manufacturing
- AI methods for detecting and classifying defects
- Implementing vision-based quality inspection systems
- Practical exercises for quality control applications
Process Optimization through Edge AI
- Introduction to industrial process optimization
- Leveraging AI for real-time monitoring and control
- Building AI-driven decision-making systems
- Practical exercises for process optimization
Deployment and Management of Edge AI Solutions
- Deploying AI models on industrial edge devices
- Monitoring and upkeep of Edge AI systems
- Troubleshooting and refining deployed models
- Hands-on exercises focused on deployment and management
Tools and Frameworks for Industrial Edge AI
- Survey of tools and frameworks (e.g., TensorFlow Lite, OpenVINO)
- Utilizing TensorFlow Lite for industrial AI tasks
- Practical exercises using optimization tools
Real-World Applications and Case Studies
- Review of successful industrial Edge AI projects
- Discussion of industry-specific use cases
- Capstone project: building and optimizing a real-world industrial AI application
Conclusion and Future Directions
Requirements
- Familiarity with fundamental AI and machine learning principles
- Practical experience with industrial automation systems
- Foundational programming proficiency (Python is recommended)
Target Audience
- Industrial engineers
- Manufacturing specialists
- AI developers
Testimonials (1)
That we can cover advance topic and work with real-life example