Enhance your TensorFlow Lite deployment with Firebase

Firebase can extend TensorFlow Lite deployments on mobile and edge devices by providing over-the-air model delivery, production performance monitoring, and A/B testing without requiring developers to build these capabilities independently. Teams can upload and publish TensorFlow Lite models through the Firebase Console or Model Management API, allowing models to be updated independently of app releases, downloaded on demand to reduce app size, and integrated into mobile applications through Firebase’s Android and iOS libraries. Firebase Performance Monitoring can record inference, preprocessing, and post-processing times across real user devices, helping developers identify performance differences by device type or operating system. Firebase Remote Config and A/B Testing can assign different model versions to user segments, measure business outcomes such as conversions or retention, and support broader rollout of the stronger-performing model. Android and iOS codelabs demonstrate these workflows using a handwritten-digit recognition application.

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