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Course Outline
Introduction to On-Device AI for Government
- Fundamentals of on-device machine learning for government applications
- Advantages and challenges of small language models in public sector contexts
- Overview of hardware constraints in mobile and IoT devices used by government agencies
Model Optimization for On-Device Deployment for Government
- Techniques for model quantization and pruning to enhance efficiency
- Knowledge distillation methods for creating smaller, more efficient models suitable for government use
- Strategies for selecting and adapting models to optimize on-device performance in government operations
Platform-Specific AI Tools and Frameworks for Government
- Introduction to TensorFlow Lite and PyTorch Mobile, tailored for government applications
- Utilizing platform-specific libraries to support on-device AI in public sector environments
- Cross-platform deployment strategies for ensuring seamless integration across government systems
Real-Time Inference and Edge Computing for Government
- Techniques for fast and efficient inference on devices, tailored to meet the needs of government operations
- Leveraging edge computing to enhance on-device AI capabilities in government settings
- Case studies of real-time AI applications in public sector projects
Power Management and Battery Life Considerations for Government
- Methods for optimizing AI applications to ensure energy efficiency in government devices
- Balancing performance and power consumption to meet the operational requirements of government agencies
- Strategies for extending battery life in AI-powered devices used by government personnel
Security and Privacy in On-Device AI for Government
- Ensuring data security and user privacy in on-device AI solutions for government use
- On-device data processing techniques to preserve user privacy in public sector applications
- Secure model updates and maintenance practices for government AI systems
User Experience and Interaction Design for Government
- Designing intuitive AI interactions to meet the needs of device users in government settings
- Integrating language models with user interfaces to enhance usability in public sector applications
- User testing and feedback processes for on-device AI solutions tailored for government use
Scalability and Maintenance for Government
- Managing and updating models on deployed devices to ensure ongoing effectiveness in government operations
- Strategies for scalable on-device AI solutions that can adapt to evolving public sector needs
- Monitoring and analytics practices for deployed AI systems to support continuous improvement
Project and Assessment for Government
- Developing a prototype in a chosen domain, with a focus on deployment on a selected device for government use
- Presentation of the on-device AI solution, highlighting its alignment with public sector workflows
- Evaluation based on efficiency, innovation, and practicality in a government context
Summary and Next Steps for Government
Requirements
- A solid foundation in machine learning and deep learning concepts for government applications
- Proficiency in Python programming to support governmental AI initiatives
- Basic understanding of hardware limitations relevant to AI deployment in public sector environments
Audience
- Machine learning engineers and AI developers working on government projects
- Embedded systems engineers with an interest in governmental AI applications
- Product managers and technical leads overseeing AI initiatives for government agencies
21 Hours
