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Duration 14 hours
Course Outline
Introduction to Edge AI Optimization
- An overview of edge AI and its associated challenges
- The critical role of model optimization for edge devices
- Case studies highlighting optimized AI models in edge applications
Model Compression Techniques
- An introduction to model compression
- Strategies for minimizing model size
- Practical exercises focused on model compression
Quantization Methods
- An overview of quantization and its advantages
- Different types of quantization (post-training and quantization-aware training)
- Practical exercises focused on model quantization
Pruning and Other Optimization Techniques
- An introduction to pruning
- Methods for pruning AI models
- Additional optimization techniques (e.g., knowledge distillation)
- Practical exercises focused on model pruning and optimization
Deploying Optimized Models on Edge Devices
- Setting up the edge device environment
- Deploying and testing optimized models
- Resolving common deployment issues
- Practical exercises focused on model deployment
Tools and Frameworks for Optimization
- An overview of relevant tools and frameworks (e.g., TensorFlow Lite, ONNX)
- Utilizing TensorFlow Lite for model optimization
- Practical exercises using optimization tools
Real-World Applications and Case Studies
- Review of successful edge AI optimization projects
- Discussion of industry-specific use cases
- A hands-on project involving the creation and optimization of a real-world application
Summary and Next Steps
Requirements
- A solid grasp of AI and machine learning fundamentals
- Prior experience in developing AI models
- Foundational programming skills (Python is preferred)
Target Audience
- AI developers
- Machine learning engineers
- System architects
Testimonials (2)
Getting people that never used AI some repetition in prompting and people that do use AI to consider different methods to using it.
Matthew Gay - Tarsus Pharmaceuticals
Course - Artificial Intelligence (AI) Overview
Working from first principles in a focused way, and moving to applying case studies within the same day
