Edge Impulse: An MLOps Platform for Tiny Machine Learning
- URL: http://arxiv.org/abs/2212.03332v3
- Date: Fri, 28 Apr 2023 22:33:47 GMT
- Title: Edge Impulse: An MLOps Platform for Tiny Machine Learning
- Authors: Shawn Hymel, Colby Banbury, Daniel Situnayake, Alex Elium, Carl Ward,
Mat Kelcey, Mathijs Baaijens, Mateusz Majchrzycki, Jenny Plunkett, David
Tischler, Alessandro Grande, Louis Moreau, Dmitry Maslov, Artie Beavis, Jan
Jongboom, Vijay Janapa Reddi
- Abstract summary: Edge Impulse is a practical MLOps platform for developing TinyML systems at scale.
TinyML are plagued by fragmented software stacks and heterogeneous deployment hardware.
As of Oct. 2022, Edge Impulse hosts 118,185 projects from 50,953 developers.
- Score: 41.93900614159169
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Edge Impulse is a cloud-based machine learning operations (MLOps) platform
for developing embedded and edge ML (TinyML) systems that can be deployed to a
wide range of hardware targets. Current TinyML workflows are plagued by
fragmented software stacks and heterogeneous deployment hardware, making ML
model optimizations difficult and unportable. We present Edge Impulse, a
practical MLOps platform for developing TinyML systems at scale. Edge Impulse
addresses these challenges and streamlines the TinyML design cycle by
supporting various software and hardware optimizations to create an extensible
and portable software stack for a multitude of embedded systems. As of Oct.
2022, Edge Impulse hosts 118,185 projects from 50,953 developers.
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