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Course Outline
Introduction to Edge AI in Industrial Automation
- Overview of Edge AI and its industrial applications
- Advantages and challenges of implementing Edge AI in industrial contexts
- Analysis 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 pertinent Edge AI frameworks and libraries
- Practical exercises focused on environment setup
Predictive Maintenance with Edge AI
- Fundamentals of predictive maintenance
- Creating AI models for monitoring equipment health
- Executing real-time fault detection and prediction
- Hands-on exercises dedicated to predictive maintenance
Quality Control Using Edge AI
- Overview of quality control practices in manufacturing
- AI techniques for detecting and classifying defects
- Building vision-based quality control systems
- Practical exercises for quality control applications
Process Optimization with Edge AI
- Introduction to process optimization strategies
- Leveraging AI for real-time process monitoring and control
- Implementing AI-driven decision-making systems
- Hands-on exercises for process optimization
Deploying and Managing Edge AI Solutions
- Deploying AI models onto industrial edge devices
- Monitoring and maintaining Edge AI systems
- Troubleshooting and refining deployed models
- Practical exercises for 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 use cases
- Hands-on exercises involving optimization tools
Real-World Applications and Case Studies
- Review of successful industrial Edge AI projects
- Exploration of industry-specific use cases
- Capstone project to build and optimize a practical industrial AI application
Summary and Next Steps
Requirements
- A solid grasp of AI and machine learning fundamentals
- Prior experience with industrial automation systems
- Foundational programming proficiency (Python is recommended)
Audience
- Industrial engineers
- Manufacturing professionals
- AI developers
14 Hours
Testimonials (1)
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