Part 2: Building Mobile AI: A Developer’s Guide to On-Device Intelligence

Subtitle: Side-by-side implementation of Secure AI on Android (Kotlin) and iOS (Swift).

In Part 1, we discussed why we need to move away from slow, cloud-dependent chatbots. Now, let’s look at how to build instant, on-device intelligence. While native code is powerful, managing two separate AI stacks can be overwhelming.

Before we jump into platform-specific code, we need to talk about the “Bridge” that connects them: Google ML Kit.

The Cross-Platform Solution: Google ML Kit

If you don’t want to maintain separate Core ML (iOS) and custom Android models, Google ML Kit is your best friend. It acts as a unified wrapper for on-device machine learning, supporting both Android and iOS.

It offers two massive advantages:

1. Turnkey Solutions: Instant APIs for Face Detection, Barcode Scanning, and Text Recognition that work identically on both platforms.
2. Custom Model Support: You can train a single TensorFlow Lite (.tflite) model and deploy ...