Machine learning is a hot technology knocking our doors and slowly reshaping the way we live.It is a core sub-area of Artificial Intelligence that enables computers to learn by themselves without being explicitly programmed.Machine learning offers a big opportunity to mobile development industry to break the shackles of mediocrity and build super apps. Let’s learn more about machine learning and its potential to change the mobile app world.
What is Core ML?
Core ML is a framework that can be used to integrate learning models in your mobile app. Designed by Apple Inc., the Core ML is available in the form of machine learning algorithms that run locally on your device.In Core ML, a trained model refers to a machine learning algorithm linked to set of training data. For example, a real estate mobile with machine learning algorithm will have a model trained on a region’s historical house prices. When you initiate a query about house prices in the real estate app, the app would be able to calculate the approximate price of the house depending on the number of bedrooms and bathrooms in the house.
The Core ML framework acts as a foundation for domain-specific framework and functionality. It supports domain-specific frameworks like Natural Language processing, Vision for image analysis, and GamePlayKit for evaluating learned decision trees. The framework is built on low-level primitive frameworks like Accelerate, Metal Performance Shaders, and BNNS.
How Core ML works?
The Core ML is designed to work with older frameworks like Accelerate, Metal Performance Shaders, and BNNS and also with new machine learning frameworks like Natural Language, GamePlayKit, and Vision.By integrating Core ML algorithms in the app, mobile app developers can use machine learning features like language processing and image recognition with minimal data science expertise.
In the Core ML framework, the trained machine learning model is used to classify input data.The Vision Framework applies classification models to images and then preprocesses images to make machine learning simple and more reliable. In cases of image identification classification, the mobile app integrated with Core ML algorithms uses open source MobileNet model which is one of the available image classification models. Mobile app developers can use any available open source image recognition models to replicate features like predictive keyboard and Face ID.To use Core ML framework, the machine learning model needs to be in the .mlmmodel format.
How Core ML technology can be used in mobile apps?
There are several applications of machine learning in mobile apps such as text mining and image analysis. The Core ML will play an important role at every place where you need to take advantage of machine learning. Here are some use case scenarios where machine learning can transform your humble mobile app into a super app.
Integrating Machine learning in e-commerce apps
The e-commerce app needs to be smart enough to give users relevant information based on their pursuits. Mobile app developers can integrate machine learning algorithms in e-commerce app to use ranking, expansion related questions, spelling correction,and query understanding to make the searching easier and intuitive for the user. The customer information about click-through rate or product sell-through rate and the behavioral data during searching and product purchase can tell you a lot about customer preferences. Based on the customer preferences, e-commerce can populate the user’s screen with products that the customer is most likely to buy.
Product Promotion and Recommendation
The recommendation system in e-commerce app is generally built on collaborating filtering method. Thee-commerce stores partners with a service provider who provides significant data service for smart recommendations. The data forms the foundation for the trained machine learning model that makes a recommendation based on trained data.
With the passing of time, the recommendation system becomes more robust and accurate with its recommendations. The reason is machine learning also takes into consideration relevant data from other machine learning models like user behavior, content site analysis, purchase patterns, and even business logic. Using the predictive analysis technology, the machine learning models make more relevant recommendations to the customer that increase sales.
Analytics and Trend forecasting
The lack of information to understand and quickly respond to latest trends is a major drawback that prevents the e-commerce businesses to take advantage of fashion trends.The data that exists in e-commerce businesses is information about upcoming tendencies and past season sales. However, there is a lot of gap between these two data mines and you need an intelligent technology that collates and combine data to give predictions in real time. Machine learning technology features that ability where it can use trained model and aggregate information from various fashion blogs, social media, and designer reports to give you trend forecasting and predictions in real-time.
Fraud detection and prevention
Online fraud is the biggest threat to online businesses. Machine learning plays an important role in building a defense system that monitors ongoing transactions and online activities and triggers alarms based on abnormal behavior pattern detections.
Mining Big Data
Big data is a useful source of information but only if you have the technology to establish relations and get useful statistics out of it. Machine learning can find non-obvious connections between data sets get statistical information from sets and understand behavior pattern of customers. For example, a food delivery app can use machine learning algorithms to analyze different kinds of data (search requests, location, age, gender, the frequency of app usage etc) to gain precise information about customer’s preferences and their distinctive behavior.
Mining of big data will also help you know about different groups of people using your app. For example, the Instagram app is more popular with the female audience below the age of 30 years. In that case, you can run promotions and offers to win female audience below the age of 30.
Use Visual and Audio recognition
Mobile users are aware of popular apps like Shazam and Snapchat. These apps are some of the oldest apps that use the audio and visual recognition technology that is part of machine learning. These apps cannot exist without machine learning. Shazam uses audio recognition technology to find the song you are looking for. Snapchat incorporates visual recognition technology for face detection. It uses Active Shape Model which is trained model using manual marking of the borders of facial features of thousands of images.
Today’s user is not happy about general things but looks for personalization in everything used by him/her. Machine learning can tap on the personalization aspect to make the mobile app more customized to user needs. For example, there are many fitness apps in the app stores that have various exercise programs, diet recommendations to keep the user fit. Leading fitness apps chart a different way to closely relate to the users and win their hearts. The fitness app with Core ML integration has several sensors to capture everyday activity data of the user. By incorporating genetic data available and sensor data, the machine learning models helps the app to create a more personalized fitness program that yields better results.
Customer service and assistance
Many users don’t like writing long letters or conversing on phone. Machine learning technology comes to help to assist users in form of virtual voice secretary or Chabot’s. They can provide users with required information or probe them by asking questions to understand their exact needs.
Machine learning is an innovative technology that is making its presence felt and touching different aspects of our lives.We are habitual of using mobile apps to simplify many of our daily tasks. The integration of Core ML will give the mobile apps the ability to understand users in a better way and reduce the dependence on touch inputs and get us the information we need in fewer taps on the screen.