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

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