Mobile versus server-side ML

Most Swift developers are writing their applications for iOS. Those among us who develop their Swift applications for macOS or server-side are in a lucky position regarding ML . They can use whatever libraries and tools they want, reckoning on powerful hardware and compatibility with interpretable languages. Most of the ML libraries and frameworks are developed with server-side (or at least powerful desktops) in mind. In this book, we talk mostly about iOS applications, and therefore most practical examples consider limitations of handheld devices.

But if mobile devices have limited capabilities, we can do all ML on the server-side, can't we? Why would anyone bother to do ML locally on mobile devices at all? There are at least three issues with client-server architecture:

• The client app will be fully functional only when it has an internet connection. This may not be a big problem in developed countries but this can limit your target audience significantly. Just imagine your translator app being non-functional during travel abroad.
• Additional time delay introduced by sending data to the server and getting a response. Who enjoys watching progress bars or, even worse, infinite spinners while your data is being uploaded, processed, and downloaded back again? What if you need those results immediately and without consuming your internet traffic? Client-server architecture makes it almost impossible for such applications of ML as real-time video and audio processing.
• Privacy concerns: any data you've uploaded to the internet is not yours anymore. In the age of total surveillance, how do you know that those funny selfies you've uploaded today to the cloud will not be used tomorrow to train face recognition, or for target-tracking algorithms for some interesting purposes, like killer drones? Many users don't like their personal information to be uploaded to some servers and possibly shared/sold/leaked to some third parties. Apple also argues for reducing data collection as much as possible.

Some of the applications can be OK (can't be great, though) with those limitations, but most developers want their apps to be responsive, secure, and useful all the time. This is something only on-device ML can deliver.

For me, the most important argument is that we can do ML without server-side. Hardware capabilities are increasing with each year and ML on mobile devices is a hot research field. Modern mobile devices are already powerful enough for many ML algorithms. Smartphones are the most personal and arguably the most important devices nowadays just because they are everywhere. Coding ML is fun and cool, so why should server-side developers have all the fun?

Additional bonuses that you get when implement ML on the mobile side are the free computation power (you are not paying for the electricity) and the unique marketing points (our app puts the power of AI inside of your pocket).