How to integrate machine learning into an android application?

Machine learning (ML) is similar to artificial intelligence (AI) which helps software applications to be more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values. Recommendation engines are a common use case for machine learning.

Other popular uses of machine learning include fraud detection, spam filtering, malware threat detection, business process automation (BPA) and predictive maintenance.

The machine learning market is growing tremendously, and many of the world’s largest tech companies are investing in advanced learning tools. These tools enable developers to integrate machine learning and machine vision into their mobile applications.

Integrating Machine Learning into Android application

  • TensorFlow Lite: TensorFlow Lite is one of the most popular open-source deep learning frameworks, which can be used on-device mobile inference. TensorFlow promises better performance by being able to leverage hardware acceleration on devices that support it.

This framework from Google can run machine learning models on Android and iOS devices. TensorFlow Lite is used on multiple devices across the world, and its set of tools is being used for all types of neural network-related apps, from image detection to speech recognition.

TensorFlow Lite enables the bulk of ML processing to take place on the device by utilising less intensive models, which do not have to rely on a server or data centre. Such models run faster, give potential privacy enhancement, consume less power, and in some cases, do not need an internet connection as well.

  • ML Kit: ML Kit is Google’s solution for integrating customised machine learning into mobile applications. This tool helps app developers to inbuilt a customised experience into their applications, which includes tools like language translation, text recognition, object detection, etc. Moreover, ML Kit helps in identifying, analysing, understanding visual and text data in real-time, and in a user privacy-focused manner, as data remains on the device. Developers can use Vision APIs under ML Kit for Video and image analysis APIs to label images and detect barcodes, text, faces, and objects. This can be used for various advanced application development and ML integration such as barcode scanning, face detection, image labelling, object detection and tracking.
  • OpenCV: OpenCV is a popular and commonly used machine learning library by developers. It is an open-source library that can store thousands of algorithms for analyzing images. Moreover, OpenCV can be used to detect faces, text, etc. OpenCV algorithms can be used for gesture and camera recognition, building 3D models, tracking eye movements and videos, etc.

What are the steps to implement machine learning?

  • Collect the training data
  • Alter the data into the required images
  • Create separate folders of images and group them
  • Re-skill the model with new images
  • Improve the model for accessible mobile apps
  • Embed. flite file into the app
  • Run the app locally and see if it detects the image

Therefore, to conclude, it can be safely stated that mobile and android app developers have numerous opportunities from innovations that machine learning offers. This is possible because of the technical abilities of the mobile applications that make things smoother for user interfaces, and experiences and empower them. Today, all the users are looking forward to a personalized experience and not a generic one. So, it is not just enough to create an application but creating the best application that can cater to the user’s needs is important. If you are looking forward to this smart integration, contact our team of experts at Augment Works. We have been providing solutions to multiple clients and have years of experience in the industry.

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Ghanshyam Sharma
Ghanshyam Sharma