Distributing & Updating CoreML models at scale with IBM Mobile Foundation

If you are a follower of what is happening in the Machine learning world, you already know Watson and Core ML. Watson is IBM’s AI platform on the Cloud and CoreML is the framework provided by Apple for running trained machine learning models on iOS devices.

Following this space closely, you are already aware that Watson services can generate trained CoreML models that can run machine learning algorithms completely offline within a mobile app.

Now that we have Watson to generate trained models and CoreML to execute these models on a device, the next question is how to keep these models current? As more data is generated, machine learning models are constantly retrained and get better and better all the time. It is imperative that mobile apps are up to date with the retrained models. It is also extremely crucial that apps do this in a secure fashion leaving no chance for attackers to meddle with the models or the data.

IBM Mobile Foundation is renowned among enterprises in keeping their apps and data secure. These capabilities of Mobile Foundation can be applied to Watson generated CoreML models to ensure that models on a device are updated constantly in a secure way. Updating a model on millions of devices at scale is also an area where Mobile Foundation excels.

Watch the video below to see how Mobile Foundation can help enterprises exploit machine learning within their apps by keeping the models current, safe and update at scale.

Last modified on August 03, 2018