Scailable.

Deploy your ML & AI models.
Instantly. Anywhere.

Convert AI & ML models to WebAssembly.
Deploy your models directly from Python or R.
Super fast. Super easy.

Join our Beta.


Which models are we talking about?

During our beta, Scailable allows you to transpile many types of fitted Scikit-learn, Statsmodels and Xgboost models to tiny, safe and efficient WebAssembly binaries through our sclblpy package - see here for an overview which models we already support.

models

Python deployment

Scikit-learn
Stats
models
Xgboost

PyTorch (ONNX)
Tensorflow (ONNX)
KERAS (ONNX)
CoreML (ONNX)


R deployment

Bayesian additive regression trees

PyTorch (ONNX)
Tensorflow (ONNX)
KERAS (ONNX)
CoreML (ONNX)


Direct Upload

Compile your Rust, C or C++ based models to Wasm.

As long as they conform to our basic Sclbl Application Binary Interface (ABI), you can deploy them to any of our runtimes.
 

  Available in beta
  Under active development



Where do you want to run your model?

When Scailable has converted your ML or AI model to a small, safe, fast and efficient WebAssembly executable, you will now be able to run it most anywhere by deploying it to one of our minimal Sclbl runtimes. During our beta, we will be running your models on our own cloud servers. But our edge, browser, bare metal and other cloud runtimes are nearing completion!

models

In the cloud

Our Sclbl cloud
On premise

AWS
Azure
Google Cloud
Your server


In a browser

Chrome
Safari
Firefox
Edge
and many more..


Mobile, IoT, Edge

MCU's such as ESP32
Raspberry Pi's

Routers running OpenWRT
Google Android
Apple IOS
Your smart fridge
and many more..


  Available in beta
  Under active development


Let's go!

If you have received a personal invite, provide your access code below and we will move you to the top of our waiting list.

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Explore our demos.

Often, actions speak louder than words. So, we put up a number of demo's to showcase what Scailable can do for you.

  1. A simple linear regression example. Here, we create a basic regression model using simulated data; give it a spin! This simple front-end supports our getting started tutorial: Scailable 101: Getting started.
  2. Scailable supports running complex models (such at Bayesian Additive Regression Trees) in the cloud, on the edge, or on a browser. Check how we can flexibly generate posterior predictives for automatic property valuation models, anytime, anywhere.
  3. Sometimes what you need is performance. We provide nothing less. The inferences from our models are extremely fast; check out some of our benchmarks.
  4. Would you like to deploy your own C or C++ based model using one of our runtimes? In this tutorial we explain how you create your own WebAssembly executable and upload it to Scailable to make it available as a REST endpoint.

About us.

It is estimated that up to 80 percent of AI and ML models never make it into production. Scailable was founded in 2019 by Robin van Emden and Maurits Kaptein to help bridge this gap.

The current team consists of a diverse group of people with backgrounds in coding, business and academia. Our shared goal is to move responsible AI to production to help it deliver on its many promises.

Maurits Kaptein
Maurits Kaptein
Robin van Emden
Arjan Haring
Arjan Haring
Arjan van den Born
Arjan van den Born
Davide Iannuzzi
Davide Iannuzzi
Fleur Hasaart
Fleur Hasaart
Petri Parvinen
Petri Parvinen