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Duration 14 hours
Course Outline
Introduction to Edge AI Optimization
- Overview of edge AI landscapes and inherent challenges
- The critical role of model optimization in edge environments
- Analysis of case studies featuring optimized models in real-world edge applications
Model Compression Strategies
- Fundamentals of model compression
- Methods for minimizing model size
- Practical exercises focused on compression techniques
Quantization Approaches
- Overview of quantization benefits and impact
- Different quantization types (post-training, quantization-aware training)
- Hands-on exercises in applying model quantization
Pruning and Advanced Optimization
- Introduction to neural network pruning
- Techniques for pruning AI model structures
- Exploring other optimization methods (e.g., knowledge distillation)
- Practical sessions on pruning and broader optimization
Deploying Optimized Models to Edge Devices
- Setting up the appropriate edge device environment
- Processes for deploying and validating optimized models
- Strategies for resolving common deployment challenges
- Hands-on activities dedicated to model deployment
Tools and Frameworks for Optimization
- Survey of key tools and frameworks (e.g., TensorFlow Lite, ONNX)
- Applying TensorFlow Lite for specific optimization tasks
- Practical exercises using various optimization tools
Practical Applications and Case Studies
- Examination of successful edge AI optimization initiatives
- Discussion of use cases across different industries
- Capstone project: building and optimizing a real-world application
Conclusion and Future Directions
Requirements
- A solid grasp of core AI and machine learning principles
- Prior experience in developing AI models
- Foundational programming proficiency (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
