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Course Outline
Introduction to On-Device AI
- Fundamentals of on-device machine learning
- Advantages and challenges of small language models
- Overview of hardware constraints in mobile and IoT devices
Model Optimization for On-Device Deployment
- Model quantization and pruning
- Knowledge distillation for smaller, efficient models
- Selecting and adapting models for on-device performance
Platform-Specific AI Tools and Frameworks
- Introduction to TensorFlow Lite and PyTorch Mobile
- Utilizing platform-specific libraries for on-device AI
- Cross-platform deployment strategies
Real-Time Inference and Edge Computing
- Techniques for fast and efficient inference on devices
- Leveraging edge computing for on-device AI
- Case studies of real-time AI applications
Power Management and Battery Life Considerations
- Optimizing AI applications for energy efficiency
- Balancing performance and power consumption
- Strategies for extending battery life in AI-powered devices
Security and Privacy in On-Device AI
- Ensuring data security and user privacy
- On-device data processing for privacy preservation
- Secure model updates and maintenance
User Experience and Interaction Design
- Designing intuitive AI interactions for device users
- Integrating language models with user interfaces
- User testing and feedback for on-device AI
Scalability and Maintenance
- Managing and updating models on deployed devices
- Strategies for scalable on-device AI solutions
- Monitoring and analytics for deployed AI systems
Project and Assessment
- Developing a prototype in a chosen domain and preparing for deployment on a selected device
- Presentation of the on-device AI solution
- Evaluation based on efficiency, innovation, and practicality
Summary and Next Steps
Requirements
- Strong foundation in machine learning and deep learning concepts
- Proficiency in Python programming
- Basic knowledge of hardware constraints for AI deployment
Audience
- Machine learning engineers and AI developers
- Embedded systems engineers interested in AI applications
- Product managers and technical leads overseeing AI projects
21 Hours
Delivery Options
Private Group Training
Our identity is rooted in delivering exactly what our clients need.
- Pre-course call with your trainer
- Customisation of the learning experience to achieve your goals -
- Bespoke outlines
- Practical hands-on exercises containing data / scenarios recognisable to the learners
- Training scheduled on a date of your choice
- Delivered online, onsite/classroom or hybrid by experts sharing real world experience
Private Group Prices RRP from €6840 online delivery, based on a group of 2 delegates, €2160 per additional delegate (excludes any certification / exam costs). We recommend a maximum group size of 12 for most learning events.
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