Machine learning is being used in many Android applications under various domains. ML is increasingly becoming a basic component in the Android environment. Some of the areas of inducting machine learning-based functions are Image Recognition apps, Object Recognition apps, Facial Recognition, Facial Expression and Sentiment Recognition, Prediction, and so on. Below are some of the most popular tools, frameworks, methods, and libraries. These are used for implementing machine learning code in Android environment to make better and efficient applications.
Tensorflow Lite is one of the most effective and lightweight solutions to give ML functionality to an Android application. Android provides one of the best Deep Learning implementations in Android Application Development. It is used under C/C++ wrapper classes and libraries in a JVM environment in Android Studio. Tensorflow Lite is highly efficient and easy to implement. The Tensorflow Lite Emulator Inference can be performed by using the Java API. Give it a try to make intelligent Android Apps.
Website Link: Tensorflowlite
Using Toolkits and Interfaces
Using cross-platform interfaces like Kivy, PySide and Chaquopy enable an app developer to implement python code in your applications. The PySide framework can be used to create an Android application through its direct implementation in Android. Qt is another highly popular toolkit for python-based Android applications which have recently converged its toolkit with PySide. Chaquopy is a python SDK for Android which works within Android Studio. It can be used for rapid app development in Android.
Google ML Kit
Google ML Kit is the perfect way to develop an application that makes use of machine learning in an easy way. Thanks to Google’s pre-trained models available in its versatile ML kit. You will be able to use Google hosted models like Cloud Vision for object recognition and image classification, Cloud Speech for speech recognition and other speech controlled functionalities, Cloud Vision Intelligence, Cloud Natural Language, NLP and Cloud Translation. The ML kit tools are easy to use and help to implement interaction between the application and user of the application. Google also provides functionalities to customize the models as per the developers’ needs.
Website Link: Google ML Kit
Fritz.ai is a mobile-based platform that can carry out image classification and recognition tasks for an Android application. Fritz.ai provides inbuilt models to speed up the development process of the app. It uses a technique in which the model learns from the data rather than following a specified set of instructions. Some of the key features of using Fritz.ai are Image labeling, Object detection, Image Segmentation.
Website Link: Fritz.ai
Deeplearning4J is another highly popular deep learning library for Android apps. It enables a developer to create and train neural networks on any android device. DeepLearning4J works on Android Studio 2.0 or higher. By using Deeplearning4J, it is easy to configure and run the Deep Learning code even on slower smartphones. Deeplearning4J can be implemented using many IDEs and tools like Eclipse, which provides larger organizations to build an application on Deeplearning4J. Deeplearning4J uses a very intuitive API which can be easily used to create multiple Perceptron based Deep Learning models.
Website Link: Deeplearning4J
Machine Learning is being used enormously and for the right reasons. AI is also being used in many android games which enhance the user interaction and applicability of the mobile applications. Implementing ML in Android Apps increases the interactivity as well as provides additional functionalities to any basic application. Android apps are becoming more dependent on ML solutions. These solutions are used with a high inclination towards ML.