Multimodal AI for Industrial Automation and Manufacturing Training Course
Multimodal AI is transforming industrial automation and manufacturing by integrating text, image, and sensor data for improved efficiency and precision.
This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level industrial engineers, automation specialists, and AI developers who wish to apply multimodal AI for quality control, predictive maintenance, and robotics in smart factories.
By the end of this training, participants will be able to:
- Understand the role of multimodal AI in industrial automation.
- Integrate sensor data, image recognition, and real-time monitoring for smart factories.
- Implement predictive maintenance using AI-driven data analysis.
- Apply computer vision for defect detection and quality assurance.
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.
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
Open Training Courses require 5+ participants.
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