Exploring Firebase MLKit on Android: Introducing MLKit (Part one)

  • Finding machine learning models that are super accurate and well trained can be not only difficult, but at the same time hard to choose which ones to use and then optimise for your platform.
  • Hosting your ML model for cloud access may also be something to bring difficult to your ML implementation. Packaging it within your app can sometimes be a more straightforward approach, but that itself comes with some drawbacks.
  • Recognise landmarks
  • Face recognition
  • Scan barcodes
  • Label images

An example of thinking about when to use on-device and cloud-based learning

implementation 'com.google.firebase:firebase-ml-vision:15.0.0'
<meta-data
    android:name="com.google.firebase.ml.vision.DEPENDENCIES"
    android:value="barcode, face, other_model_names..." />

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Joe Birch

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Joe Birch

Android Engineering Lead at Buffer, Google Developer Expert for Android & Flutter - Passionate about mobile development and learning. www.joebirch.co

Google Developers Experts

Experts on various Google products talking tech.