Machine Learning with Swift

Porting or deployment for a mobile platform

The next logical step is deploying your solution on a mobile platform (or platforms). Here, you have several considerations:

• Model memory consumption
• Data memory consumption
• Training speed (if you need on-device training)
• Inference speed
• Disk space consumption
• Battery consumption

You can profile all of this using Xcode instruments.

For information on Swift code speed optimization check out this guide: Writing High-Performance Swift Code, at: https://github.com/apple/swift/blob/master/docs/OptimizationTips.rst.

If your application includes several pre-trained models, for example, neural artistic style filters, you can use on-demand resources to store those models on the App Store and download them ...