Introduction to TinyML Training Course

Introduction to TinyML for Government

• What is TinyML?
• The significance of machine learning on microcontrollers for government operations
• Comparison between traditional AI and TinyML in a public sector context
• Overview of hardware and software requirements for government applications

Setting Up the TinyML Environment for Government Use

• Installing Arduino IDE and setting up the development environment for government projects
• Introduction to TensorFlow Lite and Edge Impulse for government applications
• Flashing and configuring microcontrollers for TinyML applications in a government context

Building and Deploying TinyML Models for Government

• Understanding the TinyML workflow for government projects
• Training a simple machine learning model for microcontrollers in government applications
• Converting AI models to TensorFlow Lite format for use in government systems
• Deploying models onto hardware devices for government operations

Optimizing TinyML for Edge Devices in Government Settings

• Reducing memory and computational footprint to enhance efficiency in government applications
• Techniques for quantization and model compression for government use
• Benchmarking TinyML model performance for government operations

TinyML Applications and Use Cases for Government

• Gesture recognition using accelerometer data in government systems
• Audio classification and keyword spotting for government applications
• Anomaly detection for predictive maintenance in government infrastructure

TinyML Challenges and Future Trends for Government

• Hardware limitations and optimization strategies for government use
• Security and privacy concerns in TinyML for government operations
• Future advancements and research in TinyML for government applications

Summary and Next Steps for Government Implementation