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Course Outline
Introduction to TinyML
- What is TinyML?
- Why deploy AI on microcontrollers for government applications?
- Challenges and benefits of TinyML in the public sector
Setting Up the TinyML Development Environment for Government Use
- Overview of TinyML toolchains suitable for government projects
- Installing TensorFlow Lite for Microcontrollers in a secure environment
- Working with Arduino IDE and Edge Impulse for government applications
Building and Deploying TinyML Models for Government Applications
- Training AI models tailored for tiny machine learning in public sector operations
- Converting and compressing AI models for use on microcontrollers in government settings
- Deploying models on low-power hardware for government projects
Optimizing TinyML for Energy Efficiency in Government Operations
- Quantization techniques for model compression to enhance efficiency for government use
- Latency and power consumption considerations for government applications
- Balancing performance and energy efficiency in public sector deployments
Real-Time Inference on Microcontrollers for Government Applications
- Processing sensor data with TinyML to support real-time decision-making for government operations
- Running AI models on Arduino, STM32, and Raspberry Pi Pico in secure government environments
- Optimizing inference for real-time applications in the public sector
Integrating TinyML with IoT and Edge Applications for Government Use
- Connecting TinyML with IoT devices to enhance government infrastructure
- Wireless communication and data transmission protocols suitable for government standards
- Deploying AI-powered IoT solutions for improved public sector efficiency
Real-World Applications and Future Trends in TinyML for Government
- Use cases in healthcare, agriculture, and industrial monitoring for government agencies
- The future of ultra-low-power AI in public sector innovation
- Next steps in TinyML research and deployment for government applications
Summary and Next Steps for Government Implementation
Requirements
- An understanding of embedded systems and microcontrollers for government applications.
- Experience with artificial intelligence or machine learning fundamentals.
- Basic knowledge of C, C++, or Python programming languages.
Audience
- Embedded engineers working in public sector projects.
- Internet of Things (IoT) developers for government initiatives.
- AI researchers supporting governmental research and development efforts.
21 Hours
