Introduction
TinyML emerged as a game-changer in 2022, enabling machine learning to run on low-power, edge devices such as microcontrollers, wearables, and IoT sensors.
🤖 Advantages of TinyML
- Energy-Efficient: ML models run on ultra-low power devices.
- Faster Processing: No need for cloud computation, reducing latency.
- Better Privacy: Data is processed locally on devices.
🚀 Use Cases
- Healthcare: Wearable devices detect heart rate abnormalities.
- Smart Homes: AI-powered automation runs on low-power sensors.
- Agriculture: AI analyzes soil and weather conditions on-site
