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
Testimonials (1)
Our trainer, Yashank, was incredibly knowledgeable. He modified the curriculum to match what we truly needed to learn, and we had a great learning experience with him. His understanding of the domain he was teaching was impressive; he shared insights from real experience and helped us solve actual problems we were facing in our work.