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
Introduction to Multimodal AI for Industrial Automation
- Overview of AI applications in manufacturing
- Understanding multimodal AI: text, images, and sensor data
- Challenges and opportunities in smart factories
AI-Driven Quality Control and Visual Inspections
- Using computer vision for defect detection
- Real-time image analysis for quality assurance
- Case studies of AI-powered quality control systems
Predictive Maintenance with AI
- Sensor-based anomaly detection
- Time-series analysis for predictive maintenance
- Implementing AI-driven maintenance alerts
Multimodal Data Integration in Smart Factories
- Combining IoT, computer vision, and AI models
- Real-time monitoring and decision-making
- Optimizing factory workflows with AI automation
AI-Powered Robotics and Human-AI Collaboration
- Enhancing robotics with multimodal AI
- AI-driven automation in assembly lines
- Collaborative robots (cobots) in manufacturing
Deploying and Scaling Multimodal AI Systems
- Choosing the right AI frameworks and tools
- Ensuring scalability and efficiency in industrial AI applications
- Best practices for AI model deployment and monitoring
Ethical Considerations and Future Trends
- Addressing AI bias in industrial automation
- Regulatory compliance in AI-powered manufacturing
- Emerging trends in multimodal AI for industries
Summary and Next Steps
Requirements
- An understanding of industrial automation systems
- Experience with AI or machine learning concepts
- Basic knowledge of sensor data and image processing
Audience
- Industrial engineers
- Automation specialists
- AI developers
21 Hours