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Starts 8 June 2025 16:49
Ends 8 June 2025
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Trends, Challenges and Best Practices for AI at the Edge
Explore trends, challenges, and optimization techniques for AI inference at the edge, focusing on machine vision applications and addressing limitations of edge devices for real-time processing.
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WeAreDevelopers
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Overview
Explore trends, challenges, and optimization techniques for AI inference at the edge, focusing on machine vision applications and addressing limitations of edge devices for real-time processing.
Syllabus
- Introduction to AI at the Edge
- Current Trends in AI at the Edge
- Challenges of AI at the Edge
- Best Practices for AI Inference at the Edge
- Machine Vision Applications at the Edge
- Future Directions and Opportunities
- Conclusion and Overview
Overview of AI edge computing
Importance of edge AI in various industries
Introduction to machine vision applications
Advances in hardware for edge computing
Popular AI models and architectures for the edge
Emerging applications in machine vision
Limitations of edge devices (memory, computation)
Real-time processing constraints
Data privacy and security issues
Optimization techniques for model efficiency
Model compression (quantization, pruning)
Knowledge distillation
Tools and frameworks for edge AI deployment
Use cases in industrial automation
Smart cameras and IoT solutions
Real-world deployment examples
Potential advancements in edge AI hardware
AI model advancements for better edge performance
New applications and markets for edge AI
Recap of key trends and challenges
Importance of staying current with edge AI developments
Subjects
Conference Talks
