ML Kit & High-Level APIs

The fastest way to add AI

You don’t always need to handle models and tensors. ML Kit (by Google) gives you ready-made, on-device AI features through a simple API — you call a method, you get a result. It works on both Android and iOS. Apple offers similar high-level APIs through Vision and Natural Language.

What ML Kit gives you out of the box

• Text recognition (OCR) — read text from images/camera.
• Barcode & QR scanning.
• Face detection — find faces, landmarks, smiles.
• Image labeling & object detection.
• Pose detection, selfie segmentation.
• Language ID, translation, smart reply.

Example: text recognition (Android)

val recognizer = TextRecognition.getClient(TextRecognizerOptions.DEFAULT_OPTIONS)
val image = InputImage.fromBitmap(bitmap, 0)

recognizer.process(image)
    .addOnSuccessListener { result ->
        for (block in result.textBlocks) {
            println(block.text)
        }
    }
    .addOnFailureListener { e -> /* handle error */ }

Example: barcode scanning (concept)

// iOS via ML Kit or Vision
let request = VNDetectBarcodesRequest { req, _ in
    let codes = req.results as? [VNBarcodeObservation]
    print(codes?.first?.payloadStringValue ?? "none")
}

On-device by default, cloud when needed

Most ML Kit features run on-device (fast, free, private). A few advanced ones can use the cloud for higher accuracy. You choose per feature.

When to use high-level APIs vs custom models

• Use ML Kit / Vision when your need matches a built-in feature (OCR, faces, barcodes, translation). It’s the fastest, most reliable path.
• Use a custom TF Lite / Core ML model when you need something specific these don’t cover (e.g. recognise your product categories).

Why this is the smart starting point

For many apps, a high-level API delivers a polished AI feature in an afternoon, with no model files, no pre-processing bugs, and automatic hardware acceleration. Start here, and only drop to custom models when you must.

Common mistakes

• Building a custom model when ML Kit already solves the problem.
• Forgetting to handle the failure callback (cameras and images are unpredictable).
• Processing every camera frame instead of throttling (battery and heat).

Summary: ML Kit and Apple’s Vision/Natural Language give you ready-made, mostly on-device AI features (OCR, faces, barcodes, translation) with a simple API. Start with these for common needs and reserve custom models for specialised tasks.