Firebase Machine Learning

Before two years ago at Google I/O 2018, Google has been introduced new feature into the Firebase. They introduced Firebase ML Kit to mobile developers as a beta version so they can use ML Kit for their apps to, solve real-world problems. In this article i hope to share some an amazing features about Firebase ML Kit. So let’s begin.

ML Kit’s processing happens on-device. This makes it fast and unlocks real-time use cases like processing of camera input. It also works while offline and can be used for processing images and text that need to remain on the device. With the combination of best machine learning models and advanced processing pipelines and offer these through easy-to use APIs to enable powerful use cases in apps.

Are you beginner to the machine learning ? It doesn’t matter. You can incorporate the functionality you need in just a few lines of code. No need to have an big knowledge about models or neural networks. If you are an expert in machine learning you can create custom models for your app using TensorFlow Lite.

First of all this is an beta version of Firebase ML Kit. This API might be changed in backward-incompatible ways and is not subject to any SLA or deprecation policy.

Use own TensorFlow Lite models for on-device inference. And just deploy model to Firebase. Firebase will dynamically serve the latest version of the model to your users, allowing you to regularly update them without having to push a new version of your app to users.

With Firebase ML and AutoML Vision Edge, you can easily train your own TensorFlow Lite image labeling models, which you can use in your app to recognize concepts in photographs. Upload training data — your own images and labels — and AutoML Vision Edge will use them to train a custom model in the cloud.

Firebase ML comes with a set of ready-to-use APIs for common mobile use cases: recognizing text, labeling images, and identifying landmarks. Simply pass in data to the Firebase ML library and it gives you the information you need. These APIs leverage the power of Google Cloud Platform’s machine learning technology to give you the highest level of accuracy.

Firebase ML has APIs that work either in the in the cloud or on the device.The text recognition, image labeling, and landmark recognition APIs perform inference in the cloud. These models have more computational power and memory available to them than a comparable on-device model, and as a result, can perform inference with greater accuracy and precision than an on-device model.

Currently ML Kit has provide 10 APIs for developer’s.

Those are per-trained models for both Android and iOS devices.

There are 3 basic steps to integrate Firebase ML Kit into your project

Quickly include the SDK using Gradle or CocoaPods.

If you’re using a vision feature, capture an image from the camera and generate the necessary metadata such as image rotation, or prompt the user to select a photo from their gallery.

Applying the ML model to your data, you generate insights such as the emotional state of detected faces or the objects and concepts that were recognized in the image, depending on the feature you used.

So that’s it…!

That’s it, thank you all..! don’t forget to leave a 👏✌️ Use Firebase🔥 Happy Coding✌️❤️

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