Course Outline
Foundations of Edge AI Optimization
- An overview of edge AI landscapes and inherent challenges
- The critical role of model optimization in edge computing
- Analysis of real-world case studies featuring optimized edge AI models
Strategies for Model Compression
- Introduction to the principles of model compression
- Methods for effectively reducing model size
- Practical exercises focused on compressing models
Quantization Approaches
- Understanding quantization and its operational benefits
- Exploring quantization types (post-training vs. quantization-aware training)
- Hands-on tasks for applying model quantization
Pruning and Advanced Optimization
- Introduction to neural network pruning
- Techniques for strategically pruning AI models
- Exploring additional optimization methods, such as knowledge distillation
- Practical implementation of pruning and optimization workflows
Deployment on Edge Hardware
- Setting up the necessary environment on edge devices
- Deploying and validating optimized models
- Resolving common deployment and integration issues
- Practical exercises for end-to-end model deployment
Essential Tools and Frameworks
- Overview of key frameworks like TensorFlow Lite and ONNX
- Applying TensorFlow Lite specifically for model optimization tasks
- Hands-on practice utilizing industry-standard optimization tools
Industry Applications and Case Studies
- Examination of successful projects in edge AI optimization
- Discussion of specific use cases across different industries
- Capstone project: Building and optimizing a practical real-world application
Concluding Summary and Future Directions
Requirements
- A solid grasp of 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 (3)
I really liked the end where we took the time to play around with CHAT GPT. The room was not set up the best for this- instead of one large table a couple of small ones so we could get into small groups and brainstorm would have helped
Nola - Laramie County Community College
Course - Artificial Intelligence (AI) Overview
Working from first principles in a focused way, and moving to applying case studies within the same day
Maggie Webb - Department of Jobs, Regions, and Precincts
Course - Artificial Neural Networks, Machine Learning, Deep Thinking
That it was applying real company data. Trainer had a very good approach by making trainees participate and compete
