iOS Machine Learning Integration Options (Cloud vs On-Device)
iOS machine learning integration options refer to the two primary approaches available to developers for incorporating ML capabilities into iOS apps: cloud-based inference, where data is sent to a remote server for processing, and on-device inference using Core ML, Apple's framework for running trained models locally. Core ML enables developers to embed machine learning models directly into an iOS app, allowing predictions to be made on the device itself without requiring a network connection. Developers choose between these approaches based on factors such as latency, privacy, and connectivity requirements.
Tutorials that teach this
Prerequisites
- Concept Benefits of Machine Learning on Mobile Devices Running machine learning models directly on a mobile device — rather than on a remote server — offers key advantages for app developers. Processing data on-device helps preserve user privacy, since sensitive information does not need to be sent over a network. It also enables apps to function without an internet connection and can reduce latency, as predictions are made locally rather than waiting for a server response.
- Concept Core ML Framework Overview