Step-by-step guide to deploying ML models using Android's ML Kit

This guide will walk you through deploying machine learning models in your Android app using Google’s ML Kit. You’ll learn how to set up your project, integrate ML Kit APIs, process input data, and run inference on-device. By the end, you’ll be equipped to add powerful AI features—such as text recognition, face detection, or barcode scanning—while maintaining user privacy and performance.

Prerequisites #

Before starting, ensure you have the following:

  • Android Studio installed (latest stable version recommended)
  • A basic understanding of Android development and Kotlin or Java
  • An Android device or emulator running API level 21 (Android 5.0 Lollipop) or higher

ML Kit supports both on-device and cloud-based processing, but this guide focuses on on-device deployment for privacy and offline use.

Step 1: Setup Your Project #

  1. Create a new Android project in Android Studio or open an existing one.

  2. Open your app-level build.gradle file (usually located at app/build.gradle).

  3. Add Google’s Maven repository to your project-level build.gradle if it’s not already present:

    allprojects {
    repositories {
        google()
        mavenCentral()
    }
}

  4. Add the ML Kit dependency for the feature you want to use. For example, to add text recognition:

    dependencies {
    implementation 'com.google.mlkit:text-recognition:16.0.1'
}

    For other features, replace the dependency with the appropriate one (e.g., face-detection, barcode-scanning, image-labeling).

  5. Sync your project to download the dependencies.

Step 2: Configure Permissions #

ML Kit features often require specific permissions. For camera-based features (like face detection or barcode scanning), add the following to your AndroidManifest.xml:

<uses-permission android:name="android.permission.CAMERA" />

If you plan to use cloud-based APIs (not covered in this guide), you’ll also need:

<uses-permission android:name="android.permission.INTERNET" />

Step 3: Prepare Input Data #

Most ML Kit APIs require an InputImage object. You can create this from various sources:

  • Bitmap
  • media.Image (from camera preview)
  • ByteBuffer or byte array
  • File on device

Here’s an example using a Bitmap:

val bitmap = // your bitmap
val image = InputImage.fromBitmap(bitmap, 0)

For camera input, use media.Image from your camera preview callback.

Step 4: Initialize and Use the ML Kit API #

  1. Create an instance of the detector for your chosen feature. For text recognition:

    val recognizer = TextRecognition.getClient(TextRecognizerOptions.DEFAULT_OPTIONS)

  2. Process the image and get results:

    val result = recognizer.process(image)
    .addOnSuccessListener { visionText ->
        // Task completed successfully
        // Extract text blocks, lines, and elements
        for (block in visionText.textBlocks) {
            for (line in block.lines) {
                for (element in line.elements) {
                    Log.d("MLKit", element.text)
                }
            }
        }
    }
    .addOnFailureListener { e ->
        // Task failed with an exception
        Log.e("MLKit", "Error: ${e.message}")
    }

    The structure of the result object varies by API (e.g., face detection returns face landmarks, barcode scanning returns barcode values).

Step 5: Handle Results and Errors #

  • Process results in the addOnSuccessListener block. Extract the relevant information and update your UI or app logic.
  • Handle errors in the addOnFailureListener block. Common issues include:
    • Insufficient permissions
    • Invalid input data
    • Model not available (rare for on-device models)

Step 6: Clean Up Resources #

After processing, release any resources used by the detector:

recognizer.close()

This is especially important for long-running apps or when processing multiple images.

Tips and Best Practices #

  • Use on-device models for privacy and offline functionality. On-device APIs are fast and secure, with no data sent to the cloud.
  • Optimize image input for better performance. Resize images to the minimum required size and ensure proper rotation.
  • Handle permissions gracefully. Request camera permission at runtime and explain why it’s needed.
  • Test on different devices. Performance may vary based on hardware capabilities.
  • Monitor battery and memory usage. On-device ML can be resource-intensive, especially with continuous camera input.

Common Pitfalls to Avoid #

  • Forgetting to add permissions in the manifest, leading to runtime crashes.
  • Not handling errors properly, which can cause your app to crash or behave unexpectedly.
  • Using cloud-based APIs without internet permission, resulting in failed requests.
  • Ignoring resource cleanup, which can lead to memory leaks.

Advanced: Using Custom TensorFlow Lite Models #

ML Kit also supports custom TensorFlow Lite models for advanced use cases:

  1. Convert your model to TensorFlow Lite format.

  2. Add the model file to your app’s assets folder.

  3. Use the ModelInterpreter API to load and run the model:

    val options = FirebaseModelInterpreterOptions.Builder(localModel)
    .build()
val interpreter = FirebaseModelInterpreter.getInstance(options)

    This allows you to run custom models for tasks not covered by ML Kit’s pre-built APIs.

Conclusion #

Deploying ML models with Android’s ML Kit is straightforward and enables powerful AI features in your apps. By following these steps, you can integrate text recognition, face detection, barcode scanning, and more—while keeping user data private and your app performant. Remember to test thoroughly and optimize for real-world use cases. With ML Kit, you can bring cutting-edge AI to your Android users with minimal effort.