Machine Learning for Mobile Apps
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.
Don’t know where to start? We curate a series of open source, yours to customize, machine learning models with example apps so you can get moving fast. Select from Image Classification, Object Detection, Text Classification, Activity Classification, Recommender Systems and more.
Your on-device machine learning models are updated, in the background, without the need for any app bundle updates. This means that you do them whenever you need to and your users do not have to wait to App approval and an App update.
Remove the hassle of tracking which environment you are updating your models to. Single click deployment and tracking from the dashboard.
Skafos can integrate with most 3rd-party cloud providers. Train and build your data science on your laptop, Create ML or whichever cloud provider you currently use.
Deliver Core ML or, for that matter, any framework of your liking (Turi Create, TensorFlow, TF-Lite, MXNet, PyTorch, for starters) to your apps.
Stop worrying about versioning. Once you upload, Skafos will version for you for tracking and delivery to the apps.
Sleep well at night knowing that your model delivery has built-in validation. Our Framework does the heavy lifting for you to prevent an update from crashing your app.