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Course Outline
Introduction to On-Device AI
- Core principles of on-device machine learning
- Benefits and obstacles associated with small language models
- Hardware limitations in mobile and IoT contexts
Model Optimization for On-Device Deployment
- Techniques for model quantization and pruning
- Using knowledge distillation to create smaller, high-performance models
- Choosing and adapting models for optimal on-device functionality
Platform-Specific AI Tools and Frameworks
- Overview of TensorFlow Lite and PyTorch Mobile
- Integrating platform-specific libraries for on-device AI tasks
- Strategies for cross-platform deployment
Real-Time Inference and Edge Computing
- Methods for achieving rapid and efficient inference on end-user devices
- Harnessing edge computing capabilities for on-device AI
- Analysis of real-time AI application case studies
Power Management and Battery Life Considerations
- Tuning AI applications for maximum energy efficiency
- Striking a balance between processing performance and power usage
- Approaches to prolonging battery life in AI-enabled devices
Security and Privacy in On-Device AI
- Protecting data integrity and user privacy
- Executing data processing locally to maintain privacy
- Implementing secure mechanisms for model updates and maintenance
User Experience and Interaction Design
- Crafting intuitive AI interactions for device users
- Seamless integration of language models with user interfaces
- Conducting user testing and gathering feedback for on-device AI features
Scalability and Maintenance
- Overseeing the management and updating of models on live devices
- Developing strategies for scalable on-device AI solutions
- Monitoring performance and analyzing data from deployed AI systems
Project and Assessment
- Building a prototype in a selected domain and preparing it for deployment on a specific device
- Presenting the developed on-device AI solution
- Assessment criteria focusing on efficiency, innovation, and practical applicability
Summary and Next Steps
Requirements
- A solid grasp of machine learning and deep learning principles
- Strong coding proficiency in Python
- Familiarity with hardware limitations relevant to AI deployment
Target Audience
- Machine learning engineers and AI developers
- Embedded systems engineers exploring AI integrations
- Product managers and technical leads responsible for AI initiatives
21 Hours
