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Duration 21 hours
Course Outline
Introduction to On-Device AI
- Core principles of machine learning on embedded devices
- Benefits and challenges associated with small language models
- Examination of hardware limitations in mobile and IoT environments
Model Optimization for On-Device Deployment
- Techniques in model quantization and pruning
- Applying knowledge distillation to create compact, high-performance models
- Choosing and adapting models to suit on-device performance requirements
Platform-Specific AI Tools and Frameworks
- Overview of TensorFlow Lite and PyTorch Mobile
- Utilizing specialized libraries for on-device AI tasks
- Strategies for deploying models across multiple platforms
Real-Time Inference and Edge Computing
- Methods for achieving rapid and efficient inference on end devices
- Harnessing edge computing capabilities for on-device AI
- Analysis of case studies featuring real-time AI applications
Power Management and Battery Life Considerations
- Tuning AI applications for maximum energy efficiency
- Striking a balance between computational performance and power draw
- Approaches to prolong battery life in AI-enabled devices
Security and Privacy in On-Device AI
- Protecting data integrity and user privacy rights
- Processing data locally on-device to preserve privacy
- Ensuring secure pathways for model updates and ongoing maintenance
User Experience and Interaction Design
- Crafting intuitive AI interactions for device users
- Integrating language models with front-end user interfaces
- Conducting user testing and gathering feedback for on-device AI features
Scalability and Maintenance
- Managing and updating models across a fleet of deployed devices
- Architecting scalable on-device AI solutions
- Implementing monitoring and analytics for live AI systems
Project and Assessment
- Building a prototype in a selected domain and preparing it for deployment on a target device
- Presenting the completed on-device AI solution
- Assessment based on efficiency, creativity, and real-world applicability
Summary and Next Steps
Requirements
- A solid understanding of machine learning and deep learning principles
- Proficiency in Python programming
- Fundamental awareness of hardware limitations in AI deployment scenarios
Target Audience
- Machine learning engineers and AI developers
- Embedded systems engineers exploring AI integration
- Product managers and technical leaders overseeing AI initiatives
