# An Introduction to Real Time Machine Learning | Embedded Applications

*An introduction to our new blog focusing on real time ML, especially for embedded applications from single-board computers to tiny microcontrollers.*

By [Real Time ML](https://paragraph.com/@realtimeml) · 2024-03-27

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There's been a lot of interest lately on large language models, with an emphasis on the large. Even [tinybox](https://tinygrad.org/) is targeting multiple GPUs with a cost over $10k.  
  
But ML can also by truly [tiny](https://www.tinyml.org/), running on embedded systems including smartphones, single-board computers, and microcontrollers. These systems typically target **real-time ML** applications. Typically, this means a batch size of one and consuming streaming sensor data as input. This use case, combined with the unique constraints of embedded systems on on power, size, and computational resources, present unique challenges and opportunities for deploying these models

In this blog, we'll share insights, tutorials, case studies, and industry news related to real-time ML in embedded systems. Whether you're an experienced ML practitioner looking to expand your knowledge or a hobbyist exploring the intersection of these technologies, we hope you will find our content informative and inspiring.

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*Originally published on [Real Time ML](https://paragraph.com/@realtimeml/an-introduction-to-real-time-machine-learning-or-embedded-applications)*