Course Code: slmsodai
Duration: 21 hours
Prerequisites:
  • 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
Overview:

Small Language Models (SLMs) are efficient and versatile AI tools that can be implemented on a variety of devices, from smartphones to IoT devices, enabling intelligent on-device applications.

This instructor-led, live training (online or onsite) is aimed at intermediate-level IT professionals who wish to deploy small language models directly onto devices with limited processing capabilities, opening up possibilities for innovative applications in various sectors.

By the end of this training, participants will be able to:

  • Understand the challenges and solutions for implementing AI on compact hardware.
  • Optimize and compress AI models for efficient on-device deployment.
  • Utilize modern AI frameworks and tools for on-device model implementation.
  • Design and develop real-time AI applications for mobile and IoT devices.
  • Evaluate and ensure the security and privacy of on-device AI systems.

Format of the Course

  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.

Course Customization Options

  • To request a customized training for this course, please contact us to arrange.
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

Sites Published:

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