sensor.jpg

A vision on AI deployment on the edge

 ·

As more and more AI models are making their way to production—i.e, they are actually being used in day-to-day business processes—an active discussion has emerged questioning “how AI models should be deployed?” (e.g., using bloated containers? By rebuilding to stand-alone executables? By using “end-to-end” platforms with strong vendor lock in?) and “where AI models should be deployed?” (e.g., on a central cloud? On edge devices? As “close” as possible to the data generating sensors?).

For 2021 we are sharing our vision of AI deployment going forward. You can download the full white-paper here, or read the summary below.

Maurits KapteinA vision on AI deployment on the edge
mnist.jpg

MNIST in the browser

 ·

With Scailable, deploying complex AI models to the browser (and beyond) is surprisingly easy.

Maurits KapteinMNIST in the browser
mnist_dev.jpg

MNIST number recognition on an ESP32

 ·

A machinelearning framework isn’t complete without its MNIST number classification demo. Still, we think ours is pretty unique: Once converted to a Scailable WebAssembly task, our MNIST model runs on almost any type of device.

Robin van EmdenMNIST number recognition on an ESP32
adminScailable news