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Use Firebase ML to train and deploy custom models, or use a more turn-key solution with the Cloud Vision APIs.

Firebase ML Diagram

Whether you are starting with an existing TensorFlow Lite model or training your own, you can use Firebase ML model deployment to distribute models to your users over the air. This reduces initial app installation size since models are downloaded by the device only when needed. It also allows you to A/B test multiple models, evaluate their performance and update models regularly without having to republish your entire app. Just upload your model to the Firebase console, and we'll take care of hosting and serving it to your app. Or if you prefer, you can deploy models directly from your ML production pipeline or Colab notebook using the Firebase Admin SDK.

AutoML Vision Edge

With AutoML Vision Edge, you can easily create custom image classification models tailored to your needs. For example, you may want your app to be able to identify different types of food, or distinguish between species of animals. Whatever your need, just upload your training data to the Firebase console and you can use Google's AutoML technology to build a custom TensorFlow Lite model for you to run locally on your user's device.

Firebase ML Icons

Firebase ML also comes with a set of ready-to-use cloud-based APIs for common mobile use cases: recognizing text, labeling images, and recognizing landmarks. Unlike on-device APIs, these APIs leverage the power of Google Cloud's machine learning technology to give a high level of accuracy. You simply pass in data to the library, which seamlessly makes a request to models running on Google Cloud, and get back the information you need–all in a few lines of code.

Firebase は、高品質アプリの開発、ユーザー層の拡大、収益の増加に貢献します。各機能はそれぞれ独立していますが、組み合わせて使用することで相乗効果を期待できます。