How to Use Machine Learning Console in Firebase?

Last Updated : 05 Dec, 2022

A powerful yet user-friendly mobile SDK called Firebase Machine Learning provides Google’s machine learning capabilities to Apple and Android apps. You only need a few lines of code to accomplish the functionality you require, regardless of your level of machine-learning expertise. To get started, you don’t need to be an expert in neural networks or model optimization. On the other hand, Firebase ML offers practical APIs that enable you to use your unique TensorFlow Lite models in your mobile apps if you are an experienced machine learning developer.

Deploy Customized Models for On-Device Use

You may utilize Firebase ML model deployment to deliver models to your users over the air whether you start with an existing TensorFlow Lite model or train your own. Since the device only downloads models when necessary, the initial size of the app installation is reduced. Additionally, it enables you to A/B test various models, assess their effectiveness, and routinely update models without needing to republish your entire app. We’ll host and provide your model to your app after you upload it to the Firebase interface. Alternatively, you can use the Firebase Admin SDK to directly deploy models from your ML production pipeline or Colab notebook.

Let’s look at them in detail:

Particulars	Features
Deploy and host customized models	Utilize your personal TensorFlow Lite models for inference on the go. We’ll host and serve your model to your app after you deploy it to Firebase. You can regularly update your models without having to push a new version of your app to users thanks to Firebase, which will dynamically serve the most recent version to your users.
Ready for production for typical use scenarios	For typical mobile use cases like text recognition, image labeling, and landmark recognition, Firebase ML includes a set of ready-to-use APIs. The Firebase ML library only requires that you supply it with some data to provide you with the necessary information. For the maximum level of accuracy, these APIs make use of the machine learning capabilities of Google Cloud.
Automatic model training	You can quickly train your own TensorFlow Lite image labeling models with Firebase ML and AutoML Vision Edge, which you can use in your app to identify ideas in images. Your own images and labels can be uploaded as training data, and AutoML Vision Edge will utilize them to build a unique model on the cloud.

Wonder what makes it different from on-device computing?

There are APIs for Firebase ML that may be used on devices or in the cloud. When we refer to an ML API as being a cloud API or on-device API, we are referring to which machine executes inference, or uses the ML model to draw conclusions about the data you provide it with. This occurs in Firebase ML either on Google Cloud or on the mobile devices of your consumers.

The inference is carried out on the cloud using text recognition, image labeling, and landmark recognition APIs. These models can do inference with more accuracy and precision than an on-device model because they have access to more computational power and memory than a comparable on-device model.

Regarding on-device custom models, Firebase ML offers two main capabilities

Custom model deployment: By uploading custom models to our servers, you can distribute them to the devices used by your consumers. The model will be downloaded on demand to the device by your Firebase-enabled app. As a result, you can keep the initial install size of your app short and change the ML model without having to republish your application.

AutoML Vision Edge: With an intuitive web interface, this service enables you to build your own on-device personalized picture categorization models. After that, you may easily host the models you make using the aforementioned service.