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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

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Small Language Models (SLMs)
Small Language Models (SLMs)