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 the Android environment to make better and more 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 the 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
You may or may not have heard about Open CV, but it is the most famous and commonly used machine learning library. It is an open-source library consisting of python bindings used for image and video processing. You can create your own set of photo and video processing algorithms using OpenCV. It helps in the face, text, and people recognition. Hence, you can do anything by implementing OpenCV, from tracking eye moments to building 3D models.
Website Link: OpenCV
Machine Learning is being used enormously and for the right reasons. AI is also being used in many android games which enhances the user interaction and applicability of 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.