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

Whether you are starting with an existingTensorFlow 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. Justupload 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 notebookusing the Firebase Admin SDK.

Firebase ML also comes with a set of ready-to-use cloud-based APIs for common mobile use cases:recognizing text,labeling images, andrecognizing 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.

Learn how to get started with ML by reviewing our technical documentation.

Understand ML pricing.