# TinyML

**Published by:** [Madhav Goyal](https://paragraph.com/@madhavg/)
**Published on:** 2021-12-18
**URL:** https://paragraph.com/@madhavg/tinyml

## Content

When normal people think of machine learning, they imagine the terminator or the matrix but when computer geek thinks of ai , they imagine big gpu clusters and tpus ,but that is not helpful and reliable for our home or iot devices as it has a lot of latency and privacy concerns ,that’s where TinyML comes in to save ML. TinyML is mainly running ml on tiny devices which have power consumption less than milli watt and are super super cheap . No one needs to plug them to power source or check daily for maintenance as they are our tiny little helpers. They can help us solve problems with ml which would have been impossible due to normal ML like monitoring marine life density or behaviors of migrating animals or even help us detect patterns in our own health. Think of wearing a single device which keeps the data local and detects anomaly in your daily bodily functions. I think this could be really helpful for detecting diseases before symptoms . I haven't talked really about the usefulness of it in our iot and home devices which I will leave it to you the reader. Most of the stuff that requires machine learning(user based) could be done locally on the device with future advancements in our chipsets which would make our experience even more smooth. One of the most common use case of TinyML is detecting anomaly in the functioning of machines for preventing machine breakdown and waking up when you say “Hey Siri” or “Hey Google”. Machine learning is about empowering us to solve problems that we could only think of earlier and TinyML only increases that further. I would like to Thank Pete Warden for reviewing the blog and inspiring me with his book about TinyML . And I have been chosen as one of the Ambassadors of Full Steam Ahead so if you have an existing STEM team Signup at https://forms.gle/Gr31PUz5koNfjaCZA

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