Want to know more about ML at the edge?

Machine Learning is an emergent, rapidly changing field, and building a model is hard enough. Now you have to figure out how to make it work on an edge device, manage it in production, and make sure your architecture is both scalable and performant? Fortunately, we have a team of experienced, dedicated data scientists and software engineers who understand the unique challenges and opportunities of running ML on the edge, and how to make it work in your app. Learn more below or reach out to us today.

The Skafos.ai team is freshly back from Apple’s World Wide Developer Conference known as WWDC (or “dub dub” for those on the ground) held this year in San Jose, California. As always, there are a number of announcements but there are too many to list here so let’s focus on ones that get the Skafos.ai team excited.
by Michael J. Prichard
What if you’ve used IBM Watson to train your model, but you need to deliver it into an app you already have? How do you incorporate improved models into your app without having to re-submit to the app store? What if you need to rollback to a previous version? Stick around, because I am going to walk you through it.
by Dr. Miriam Friedel
Director of Data Science, Dr. Miriam Friedel, walks you through how Skafos can seamlessly integrate with AWS SageMaker. Use SageMaker to train your model, use Skafos to deliver it.
by Dr. Miriam Friedel
Getting Turi Create to train a model, with a GPU, on Colab was no small feat. In the rest of this post I will share how I was able to conquer the beast and train an Image Classification model on a Tesla T4 GPU. At the end, I link you directly to the code so you can play around for yourself.
by Tyler Hutcherson
Since the beta launch of the Skafos platform in January, 2019, we had over 400 users sign-up to use the platform, and have received feedback from many of you. Thank you! Based on your feedback, yesterday we launched a new and improved version of our platform to simplify and streamline the delivery of machine learning to the edge.
by Dr. Miriam Friedel
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 Skafos.ai for delivery.
by Tyler Hutcherson
This tutorial walks you through the process of taking data from Google’s Open Images Dataset and adjusting the bounding box coordinate system for use within the Turi Create Framework. We will do this using a nifty toolkit called OIDv4 Toolkit to retrieve the data we need.
by Dr. Miriam Friedel