  Although deep neural networks are typically computationally expensive to use,
technological advances in both the design of hardware platforms and of neural
network architectures, have made it possible to use powerful models on edge
devices. To enable widespread adoption of edge based machine learning, we
introduce a set of open-source tools that make it easy to deploy, update and
monitor machine learning models on a wide variety of edge devices. Our tools
bring the concept of containerization to the TinyML world. We propose to
package ML and application logic as containers called Runes to deploy onto edge
devices. The containerization allows us to target a fragmented
Internet-of-Things (IoT) ecosystem by providing a common platform for Runes to
run across devices.
