Skafos makes it simple for you to build ML-powered apps, whether you are prototyping or delivering to millions of devices.

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Personalize user choices, identify their actions, or even the things that they say. Classify whole images, objects inside images, or even just their look and feel. Skafos is the fastest way to the best parts of machine learning.

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Quickly get going
Learn by doing. Start with one of our example ML-apps and make it your own.
  • Learn how machine learning works in your app by using an example ML-powered app and iterate.
  • Install our framework to remove the tedious and time-consuming work.
Model delivery built-in
No more app store updates.

Skafos manages your model deployments through automated background updates to the devices and apps using your models.

  • Push updates as often as you need.
  • Don’t fight app store reviews just to make adjustments to your models and overall user experience.
  • Manage model versions in one easy-to-use dashboard.
Build or bring your own models
You already have a ML model, but deployment sucks. Use Skafos to deliver in minutes.
  • Bring your data, or your model, or both
  • One user or millions, we grow with you

A brief tutorial on Image Classification for iOS Developers

Here, we will walk you through how to get started with Skafos Quickstart models, adding the right libraries, importing additional data, and creating labels so you can customize the model to suit your use case.

by Dr. Miriam Friedel

Out-of-the box, provides users with a library of starter model examples that cover a variety of machine learning techniques that you can integrate into your iOS application. The purpose of these models is to get users up and running with an ML enabled iOS app as soon as possible! However, it’s quite possible that a new user (like you) will have an idea for a mobile app that none of the available models address. On Skafos, that’s no problem at all! Below I will explore an example of how to build a custom Core ML model and deploy it with the Skafos framework.

by Tyler Hutcherson

This post is the 3rd installment in a series devoted to activity classification leveraging machine learning and on-device sensor data. In part 1, I shared about the tedious data collection process. Then in part 2, I showed you how to put the data to good use, training an activity classification model with Apple’s Turi Create. This last post will take you through an example iOS application (for iPhone) that leverages the machine learning model you’ve prepared and for delivery.

by Tyler Hutcherson

As a software engineer, I am often learning new concepts, new languages, new frameworks, new design patterns, new…well, you get the idea. Applying these new concepts makes writing code a constant experiment in whatever new idea has caught my attention. Code I wrote 6 months ago might look very different were I to write it again today.

Through experimentation I get to validate or invalidate new concepts and learn where and when to apply them. Then I start again, layering in the next, new concept. From this perpetual learn→apply→(in)validate cycle a personal axiom has emerged, something I call the RICI Principle.
The RICI Principle is about the process of learning.

by David Sison