How to integrate Machine Learning in a Mobile App

September 23, 2022

Machine Learning has a potential that if integrated into our daily lives, the world around can change in a matter of few years. The most intriguing aspect of this fact is machine learning has not completely unfolded to the world as yet. In a nutshell, ML is the present and future of mobile apps.

Machine Learning has a potential that if integrated into our daily lives, the world around can change in a matter of few years. The most intriguing aspect of this fact is Machine Learning has not completely unfolded to the world as yet. Mobile app developers around the globe are learning and trying to integrate ML in the mobile applications. We, as always, thought to ease the struggle for those who are learning app development or growing into it.

Here is the guide on ‘How to integrate Machine Learning in Mobile Apps’. There are several open-sourced ML libraries available in the market which can be used in app development process. These libraries open ways for new ideas to get cradled with the latest app development technology and giving an edge of ML to it.

Most popular of all the platform services are:


1. Core ML

Core ML allows you to integrate trained ML models into your mobile app.

It accommodates domain-specific frameworks like

  • Vision for image analysis,
  • Foundation for natural language processing,
  • GameplayKit for evaluating learned decision trees

Core ML is have been improvised to perform smoothly on-device to occupy less memory and lower power consumption.


2. TensorFlow

TensorFlow is used when your app needs to perform numerical computation on the basis of data flow graphs. TensorFlow was programmed by engineers on Google Brain Team to perform neural network research, but it is very generic which is why it can be deployed in numerous domains with ease.

It is an open-source library which means everybody is in for a treat! It has a flexible architecture and performs complex computations with ease.


3. Microsoft Cognitive Services

  • Face API: Face API detects the faces and compares them visually similar face pictures.
  • Emotion API: This feature captures the facial expressions of the user and comprehends it to improvise user engagement.
  • Content Moderator: Content Moderator API use the content that user process on social media, chat or gaming platform and monitor it to rule out the inappropriate content.
  • Computer Vision API: This API recognizes the contents of images and creates tags and develops meaningful info describing it.
  • LUIS: LUIS is an ML-based service that helps developers install Natural Language Processing (NLP) features in their mobile apps, IoT devices, and chat-bots.


4. Amazon ML services

Amazon Machine Learning services allow app developers to formulate ML models by using wizards and visualization tools. They have floated numerous APIs in the market for machine learning app developers that helps in fetching predictions for apps without execution of custom prediction generation code.


5. TCS Ignio

Ignio by TCS is self-learning and adaptive ML-based platform which was built to optimize IT operations. When it is integrated into any app, it grasps all the info and lowers the chances of knowledge gaps. It resolves complex problems on its own.
In a nutshell, ML is the present and future of mobile apps. If you have any further queries regarding these platforms, reach us here.

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