Edge Impulse: An MLOps Platform for Tiny Machine Learning
This addresses the challenge of scaling TinyML systems for developers working on embedded and edge devices, though it appears incremental as it builds on existing MLOps concepts.
The paper tackles the problem of fragmented software stacks and heterogeneous hardware in TinyML workflows by presenting Edge Impulse, a cloud-based MLOps platform that streamlines development and deployment, with results including hosting 118,185 projects from 50,953 developers as of Oct. 2022.
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.