vidushhipanda
TinyML
TinyML (Tiny Machine Learning) is transforming how AI works on constrained hardware. Instead of relying on cloud servers, TinyML models run locally on microcontrollers, IoT sensors, and edge devices with limited memory and processing power. This allows applications to deliver real-time predictions, lower latency, energy efficiency, and improved privacy.
Deploying TinyML on edge devices, however, is not straightforward. Developers face challenges like tiny memory sizes (KBs instead of GBs), limited compute capability, and strict power budgets. To overcome these constraints, following proven best practices is critical.
link to the full blog:
www.linkedin.com/in/vidushhi-panda-a9012730b/
TinyML
TinyML (Tiny Machine Learning) is transforming how AI works on constrained hardware. Instead of relying on cloud servers, TinyML models run locally on microcontrollers, IoT sensors, and edge devices with limited memory and processing power. This allows applications to deliver real-time predictions, lower latency, energy efficiency, and improved privacy.
Deploying TinyML on edge devices, however, is not straightforward. Developers face challenges like tiny memory sizes (KBs instead of GBs), limited compute capability, and strict power budgets. To overcome these constraints, following proven best practices is critical.
link to the full blog:
www.linkedin.com/in/vidushhi-panda-a9012730b/