Evgeni Gousev's guest appearances

Evgeni Gousev

Momenta I Digital Thread

Orchestrate all the Things podcast: Connecting the Dots with George Anadiotis

The IoT Podcast

Anurag Gupta

Gary Marcus

Abhishek Singh

Andrew Ng

Abe Gong

Ryan Wright

Sergey Nazarov

Ben Lorica

Trent McConaghy

Dominik Schiener

Emil Eifrem

Dan Erickson

Vishal Gupta

Brian Platz

David Scales

OwlTail

Momenta's podcast with Evgeni Gousev, Chairman, tinyML Foundation. The foundation, with over 10,000 members, guides the ecosystem's technological approach and educates key audiences. Tiny machine learning technologies and applications consume extremely little power, generally in the mW range or less. Find out more about this interesting community.

Evgeni Gousev, Chairman, tinyML Foundation - tinyML Ecosystem

Blair Newman

Being able to deploy machine learning applications at the edge is the key to unlocking a multi-billion dollar market. TinyML is the art and science of producing machine learning models frugal enough to work at the edge, and it's seeing rapid growth. Edge computing is booming. Although the definition of what constitutes edge computing is a bit fuzzy, the idea is simple. It's about taking compute out of the data center, and bringing it as close to where the action is as possible. Whether it's stand-alone IoT sensors, devices of all kinds, drones, or autonomous vehicles, there's one thing in common. Increasingly, data generated on the edge are used to feed applications powered by machine learning models.  There's just one problem: machine learning models were never designed to be deployed on the edge. Not until now, at least. Enter TinyML. Tiny machine learning (TinyML) is broadly defined as a fast growing field of machine learning technologies and applications including hardware, algorithms and software capable of performing on-device sensor data analytics at extremely low power, typically in the mW range and below, and hence enabling a variety of always-on use-cases and targeting battery operated devices. Article published on ZDNet

Being able to deploy machine learning applications at the edge is the key to unlocking a multi-billion dollar market. TinyML is the art and science of producing machine learning models frugal enough to work at the edge, and it's seeing rapid growth. Edge computing is booming. Although the definition of what constitutes edge computing is a bit fuzzy, the idea is simple. It's about taking compute out of the data center, and bringing it as close to where the action is as possible. Whether it's stand-alone IoT sensors, devices of all kinds, drones, or autonomous vehicles, there's one thing in common. Increasingly, data generated on the edge are used to feed applications powered by machine learning models. There's just one problem: machine learning models were never designed to be deployed on the edge. Not until now, at least. Enter TinyML. Tiny machine learning (TinyML) is broadly defined as a fast growing field of machine learning technologies and applications including hardware, algorithms and software capable of performing on-device sensor data analytics at extremely low power, typically in the mW range and below, and hence enabling a variety of always-on use-cases and targeting battery operated devices. <a href="https://www.zdnet.com/article/machine-learning-at-the-edge-tinyml-is-getting-big/" rel="nofollow" target="_blank">Article published on ZDNet</a>

Machine learning at the edge: TinyML is getting big. Featuring Qualcomm Senior Director Evgeni Gousev, Neuton CTO Blair Newman and Google Staff Research Engineer Pete Warden