is the comprehensive app development platform by Google, which provides you infrastructure and libraries to make app development easier for both Android and iOS. and You can also upload your model to Firebase using the Firebase ML
A/B test multiple model versions
When you iterate on your machine learning model and come up with an improved model, you may feel very eager to release it to a production right away. However, it is not rare that a model may perform well on test data but fail badly in production. Therefore, the best practice is to roll out your model to a smaller set of users, A/B test it with the original model and closely monitor how it affects your important business metrics before releasing it to all of your users.Firebase A/B Testing enables you to run this kind of A/B testing with minimal effort. The steps required are:
- Upload all TensorFlow Lite model versions that you want to test to Firebase, giving each one a different name.
- Setup Firebase Remote Config in the Firebase console to manage the TensorFlow Lite model name used in the app.
- Update the client app to fetch TensorFlow Lite model name from Remote Config and download the corresponding TensorFlow Lite model from Firebase.
- Update the client app to fetch TensorFlow Lite model name from Remote Config and download the corresponding TensorFlow Lite model from Firebase.
- Setup A/B testing in the Firebase console.
- Decide the testing plan (e.g. how many percent of your user base to test each model version).
- Decide the metric(s) that you want to optimize for (e.g. number of conversions, user retention etc.).
- Decide the testing plan (e.g. how many percent of your user base to test each model version).
Next steps
Check out this codelab ( version) to learn step by step how to integrate these Firebase features into your app. It starts with an app that uses a TensorFlow Lite model to recognize handwritten digits and show you:- How to upload a TensorFlow Lite model to Firebase via the Firebase Console and the Firebase Model Management API.
- How to dynamically download a TensorFlow Lite model from Firebase and use it.
- How to measure pre-processing, post processing and inference time on user devices with Firebase Performance Monitoring.
- How to A/B test two versions of a handwritten digit classification model with Firebase A/B Testing.
Acknowledgements
Amy Jang, Ibrahim Ulukaya, Justin Hong, Morgan Chen, Sachin Kotwani Vollständiger Original-Bericht
Ausführliche Details, Code-Beispiele & Hersteller-Stellungnahme auf blog.tensorflow.org.
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