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Course Outline
Introduction to Edge AI Optimization
- Overview of edge AI and its challenges
- Importance of model optimization for edge devices
- Case studies of optimized AI models in edge applications
Model Compression Techniques
- Introduction to model compression
- Techniques for reducing model size
- Hands-on exercises for model compression
Quantization Methods
- Overview of quantization and its benefits
- Types of quantization (post-training, quantization-aware training)
- Hands-on exercises for model quantization
Pruning and Other Optimization Techniques
- Introduction to pruning
- Methods for pruning AI models
- Other optimization techniques (e.g., knowledge distillation)
- Hands-on exercises for model pruning and optimization
Deploying Optimized Models on Edge Devices
- Preparing the edge device environment
- Deploying and testing optimized models
- Troubleshooting deployment issues
- Hands-on exercises for model deployment
Tools and Frameworks for Optimization
- Overview of tools and frameworks (e.g., TensorFlow Lite, ONNX)
- Using TensorFlow Lite for model optimization
- Hands-on exercises with optimization tools
Real-World Applications and Case Studies
- Review of successful edge AI optimization projects
- Discussion of industry-specific use cases
- Hands-on project for building and optimizing a real-world application
Summary and Next Steps
Requirements
- An understanding of AI and machine learning concepts
- Experience with AI model development
- Basic programming skills (Python recommended)
Audience
- AI developers
- Machine learning engineers
- System architects
14 Hours
