Course Outline
Introduction to Edge AI Optimisation
- Overview of edge AI and its inherent challenges.
- The significance of model optimisation for edge devices.
- Case studies illustrating optimised AI models in edge applications.
Model Compression Techniques
- Introduction to the concept of model compression.
- Strategies for reducing model size.
- Practical exercises focused on model compression.
Quantisation Methods
- Overview of quantisation and its advantages.
- Types of quantisation (post-training, quantisation-aware training).
- Practical exercises for model quantisation.
Pruning and Additional Optimisation Techniques
- Introduction to pruning.
- Methods for pruning AI models.
- Other optimisation techniques (e.g., knowledge distillation).
- Practical exercises for model pruning and optimisation.
Deploying Optimised Models on Edge Devices
- Preparing the edge device environment.
- Deploying and testing optimised models.
- Resolving common deployment issues.
- Practical exercises for model deployment.
Tools and Frameworks for Optimisation
- Overview of key tools and frameworks (e.g., TensorFlow Lite, ONNX).
- Utilising TensorFlow Lite for model optimisation.
- Practical exercises using optimisation tools.
Real-World Applications and Case Studies
- Review of successful edge AI optimisation projects.
- Discussion of industry-specific use cases.
- A hands-on project involving the construction and optimisation of a real-world application.
Summary and Next Steps
Requirements
- A solid understanding of AI and machine learning concepts.
- Experience in developing AI models.
- Basic programming skills (Python is recommended).
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
