Edge AI on Mobile: Using TensorFlow Lite and Core ML for Low-Latency Real-Time Features

The Shift to On-Device Machine Learning

Traditional mobile AI features send user data (like photos or audio) to a cloud server for processing and then wait for the result—creating latency and raising privacy concerns. Edge AI brings the model directly to the device, running inference instantly on the mobile hardware.

Key Benefits of Low-Latency On-Device AI

The Platform Tools: TensorFlow Lite and Core ML

To enable Edge AI, you must leverage platform-specific optimization tools. TensorFlow Lite is the optimized framework for Android, and Core ML is Apple's native framework for iOS. Both are designed to run trained models efficiently on constrained mobile hardware.

import CoreML

let model = try MyImageClassifier(configuration: .init())
let prediction = try model.prediction(image: inputImage)

Optimization is Key

Running AI on mobile requires tiny, optimized models. This involves techniques like model quantization, pruning, and using specialized mobile-first architectures to keep the app size manageable and execution fast.