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title: Enhance your TensorFlow Lite deployment with Firebase
description: A walkthrough of using Firebase to extend TensorFlow Lite mobile deployments with three capabilities: over-the-air model updates via Firebase ML's Model...
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# Enhance your TensorFlow Lite deployment with Firebase

**[Firebase](https://daily.dev/sources/firebase-blog)** · 7 min read · 0 upvotes · 0 comments

## Summary

A walkthrough of using Firebase to extend TensorFlow Lite mobile deployments with three capabilities: over-the-air model updates via Firebase ML's Model Management API, on-device inference speed measurement using Firebase Performance Monitoring, and A/B testing multiple model versions using Firebase Remote Config and A/B Testing. Includes Android and Python code snippets, plus links to Android and iOS codelabs demonstrating a handwritten digit classifier that integrates all three features.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://firebase.blog/posts/2020/06/enhance-your-tensorflow-lite-deployment-with-firebase>

## Questions this post answers

### How do I deploy a TensorFlow Lite model to my app without bundling it into the app binary?

Upload the TensorFlow Lite model to Firebase ML using the Firebase Console or the Model Management API in Python, then download it in the app at runtime with FirebaseCustomRemoteModel and FirebaseModelManager.getLatestModelFile(), and initialize a TensorFlow Lite Interpreter with the downloaded file. This supports over-the-air updates and lazy-loading models to reduce app size.

_daily.dev surfaces practical guides like this for teams shipping on-device ML models over the air._

### How can I A/B test two versions of a TensorFlow Lite model in production?

Upload each model version to Firebase ML with a distinct name, then use Firebase Remote Config to control which model name the client fetches, and set up a Firebase A/B Testing experiment to split traffic (e.g., 50/50) and track business metrics like conversions or retention. Once a winner is confident, roll it out to all users from the Firebase console.

_Developers comparing model rollout strategies can find similar deployment tactics on daily.dev._

### How do I measure TensorFlow Lite inference speed on real user devices instead of just test devices?

Use Firebase Performance Monitoring by wrapping the interpreter.run() call with a custom trace, created via firebasePerformance.newTrace("model_inference") and stopped after inference completes. Firebase aggregates this data across the user base so slow devices or OS versions with degraded inference speed can be identified from the console.

_daily.dev helps mobile ML engineers track techniques for profiling model performance in production._

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---

Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#mobile](https://daily.dev/tags/mobile), [#firebase](https://daily.dev/tags/firebase), [#tensorflow](https://daily.dev/tags/tensorflow), [#ab-testing](https://daily.dev/tags/ab-testing)

[View this post on daily.dev](https://daily.dev/posts/enhance-your-tensorflow-lite-deployment-with-firebase-wjcpk7aaq)

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