/technologies/ai-machine-learning/b/blog/posts/embedded-ml-frameworks-comparison-edge-impulse-vs-tinyml-vs-others(Image credit: Pixabay ) Embedded machine learning (ML) has evolved rapidly over the last decade, and rather than relying on cloud inference, developers are increasingly deploying AI models directly on low-power devices. This move is the result...en-USTelligent Community 12https://community.element14.com/technologies/ai-machine-learning/b/blog/posts/embedded-ml-frameworks-comparison-edge-impulse-vs-tinyml-vs-othersWed, 26 Nov 2025 17:27:05 GMT93d5dcb4-84c2-446f-b2cb-99731719e767:2f90f07b-43d2-41e5-b963-d3c928868d5cembeddedguy0<p>Nice topic to discuss.</p> <p>In my recent project about LEDs, I have tried some platforms for Edge ML.&nbsp;</p> <p>For some quick deployment platforms such as Edge-impulse are useful but for in depth and customization one can opt for customized models and libraries such as TF-lite micro.</p> <p>But the second one is very lengthy process ranging from data collection, filtering, training model, converting model and model deployment. I have gave up quite some time inbetween this process.</p> <p>My understanding is that if not full, but engineers need some tools that help to create this entire process easy and comparatively faster.</p><img src="https://community.element14.com/aggbug?PostID=29379&AppID=217&AppType=Weblog&ContentType=0" width="1" height="1">