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Bring Machine Learning to iOS apps using Apache MXNet and Apple Core ML

Bring machine learning to iOS apps using Apache MXNet and Apple Core ML.

  • With the release of Core ML by Apple at WWDC 2017, iOS, macOS, watchOS and tvOS developers can now easily integrate a machine learning model into their app.
  • Members of the MXNet community, including contributors from Apple and Amazon Web Services (AWS), have collaborated to produce a tool that converts machine learning models built using MXNet to Core ML format.
  • In this blog post, we explain how to set up an environment to convert MXNet models into Core ML, convert an existing model, and then import it into a sample iOS app written in Swift.
  • With Xcode installed, if you double-click this model file, you can get more information about it, such as its size, name, and parameters, which would usually be used within your Swift code: – – The sample iOS app was written in Swift using Xcode 9 beta 6 on a Mac…
  • As this blog post demonstrates, you can now build and train sophisticated machine learning models on AWS using MXNet, convert them to Core ML format with the MXNet to Core ML converter tool, and quickly import them into Xcode.

With the release of Core ML by Apple at WWDC 2017, iOS, macOS, watchOS and tvOS developers can now easily integrate a machine learning model into their app. This enables developers to bring intelligent new features to users with just a few lines of code. Core ML makes machine learning more accessible to mobile developers. It also enables rapid prototyping and the use of different sensors (like the camera, GPS, etc.) to create more powerful apps than ever.

With the release of Core ML by Apple at WWDC 2017, iOS, macOS, watchOS and tvOS developers can now easily integrate a machine learning model into their app. This enables developers to bring intelligent new features to users with just a few lines of code. Core ML makes machine learning more accessible to mobile developers. It also enables rapid prototyping and the use of different sensors (like the camera, GPS, etc.) to create more powerful apps than ever.

Members of the MXNet community, including contributors from Apple and Amazon Web Services (AWS), have collaborated to produce a tool that converts machine learning models built using MXNet to Core ML format. This tool makes it easy for developers to build apps powered by machine learning for Apple devices. With this conversion tool, you now have a fast pipeline for your deep-learning-enabled applications. You can move from scalable and efficient distributed model training in the AWS Cloud using MXNet to fast run time inference on Apple devices.

To support the release of the converter tool we decided to build a cool iOS app. We were inspired by a previous AWS AI Blog post, Estimating the Location of Images Using MXNet and Multimedia Commons Dataset on AWS EC2, that showcases the LocationNet model to predict the location where pictures were taken.

In this blog post, we explain how to set up an environment to convert MXNet models into Core ML, convert an existing model, and then import…

Bring Machine Learning to iOS apps using Apache MXNet and Apple Core ML

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