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Various Ways of Implementing ML/AI in Android Applications

Do you know? According to Forbes Advisor, 65% of consumers trust businesses that use AI (Artificial Intelligence) technology. No doubt, lots of companies are starting to implement AI in their products.

Not only on products related to daily life, but also on products that are usually used for work. Call it Android Studio with its Studio Bot, Google Workspace with its Duet AI, and Photoshop with its Generative Fill.

It is undeniable that the use

of AI really helps us to get the job done much faster. Agree or totally agree? So, the question is “are we only going to be users and tasters of these new tools?”

The author’s hope is of course¬† Canadian CTO CIO Email Lists¬†at we are not only spectators, but also developers who can take advantage of AI. At least start from a simple machine learning level, such as image classification, text recognition, or object detection.

Interestingly, for those of you who don’t have a deep understanding of machine learning, you can also apply this technology. Currently there are many tools that you can use to implement machine learning in Android applications. Starting from the simple, complex, to flexible. How, have you started to wonder about the kinds of ways you can do it? Come on, let’s talk!

ML Kit for implementing ML/AI on Android

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ML Kit is a machine learning framework developed by Google for Android and iOS devices. It provides a set of APIs that provide machine learning features, such as image recognition and natural language processing (NLP).

This is the easiest way to add machine learning features to your Android app. This is because you don’t need to understand how to create your own models to implement machine learning, it’s all built into the framework. By using ML Kit, you may never imagine that implementing ML can be this easy.


TensorFlow Lite

In ML Kit, models are provided built-in. The CRYP Email List model referred to here is a machine learning algorithm that has previously undergone a training process with certain training data so that it is ready to be used to make predictions on new data.

Then, what if you want to use a separate model? One answer is to use TensorFlow Lite. As the name suggests, TensorFlow Lite is a lightweight and efficient version of the Tensorflow framework which is often used by ML Developers to develop and deploy models.

It is designed in such a way that allows us to run models on devices with limited resources, such as mobile phones and embedded systems. In addition, it supports various types of machine learning, such as image recognition, audio recognition, and natural language.

This library can be run on mobile devices, such as Android and iOS or iOT devices, such as Raspberry Pi and Arduino. Some examples of applications that use TensorFlow Lite are GMail, Google Assistant, Google Nest, and Shazam. Of course the names are familiar, right.

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