Have you ever wondered how Spotify determines which songs to display to you based on your tastes, or how fitness and health applications are able to offer you fitness suggestions perfectly tailored to your current goals (even if you haven't said them out loud)? This application "wow factor" has been made possible in large part due to the rise of machine learning technology!

In this article, we'll dive into the role of Artificial Intelligence (AI) and Machine Learning (ML) in cutting-edge mobile application development and their impact on the mobile app industry.

What's with all the ML hype?

Today, our mobile devices can recognize speech requests, help us stay on top of our complicated schedules, and even serve as a translator. Based on the early success of this tech, a growing number of entities are investing in producing AI-based mobile applications.

According to one estimate, the global market for ML is expected to grow by more than 20 billion dollars, and the market has seen an annual growth rate of 44.06 percent since 2017. The reason is that machine learning fabricates user-friendly platforms with improved customer experience and consistency.

Impact of AI and ML in Mobile Application Development

Companies are embedding machine learning to create customized apps that can understand user behavior to provide a more personalized experience and enhance user interactivity.

Personalization

Machine learning helps with the classification of users based on information accessed from user behavior on applications and social media. This collected information allows you to learn about your customers' interests, the manner in which they use your product, and their preferences as users. This information is gathered through ML algorithms and can be used further to improve and shape your product’s content.

An Uber Eats delivery courier heading to a destination on their bike.
Photo by Kai Pilger on Unsplash

Applications using ML for personalization

UberEats, Uber, and Taco Bell are some popular apps in this category. Uber and UberEats are ML integrated apps that show estimated arrival and delivery time with real-time monitoring on the map. Taco Bell uses an ML bot to take orders and offer suggestions based on preferences.

Data Mining

Data mining encompasses tasks like collection, storage, maintenance, and analysis of data. ML algorithms collect a large dataset of clients and identify patterns by categorizing the data.

Applications using ML for data mining

Travel applications are the best example here since operators acquire business intelligence to adjust improved tours and schedules.

Improved User Engagement

Some machine learning features can attract users to engage with your app on a daily basis. Conversational and AI virtual assistants engage with users who are lost in a product and help customers understand products.

Applications using ML for user engagement

Facebook and Amazon use machine learning to handle smart requests and boost user engagement. Digital assistants can help users write long emails and make calls. Prisma offers a bot that can resize a photo and apply filters to it for you. Mona searches products on more than hundred websites to handle shopping procedures for you.

Upgraded Security

Machine learning can streamline security and authentication for almost any type of application. Face detection, fingerprint access, biometric info, and audio/video/voice recognition are some features that help in detecting fraudulent activity and ensuring safe access to confidential data.

Applications using ML for security

Applications like TurboID and BioID utilize eye recognition and face detection to let users securely access websites and other apps.

try our app estimate calculator CTA image

Automated Reasoning

Mobile app developers can use ML to managed the execution of simple functions and tasks. Automated reasoning also helps in collecting insights from historical data and using them to solve a problem.

Applications using ML for task automation

Uber, Google Maps, and similar navigation apps use these algorithms of automated reasoning to help users reach their destination as quickly as possible through obtaining travel data.

Evaluating Customer Behavior

Companies inspect user behavior by looking over data (age, gender, preference, requests, search items, app usage frequency, etc.) to provide customers with a coherent, logical experience. NLP and machine learning algorithms can be integrated in app architecture to observe user behavior and make necessary modifications in app functionality.

Applications that use ML to evaluate customer behavior

Netflix uses a recommendation structure to make movie/show suggestions, and Youboox also uses the same engine to recommend books.

Steps to Develop an ML Mobile Application

The vital process when developing ML-based mobile apps is to train algorithms. The basic development process, however, involves the following steps.

Steps

Functionality


Gather and filter data

This step calls for collecting random, error-free data. Data must not contain replicated values.


Select a suitable model and train it

To solve a problem or construct a correct prediction, an optimal algorithm must be chosen and trained.

TensorFlow Hub contains a huge repository of pre-trained models that works best with Android interfaces.


Analyze model for real-time data

Evaluate how your model will perform in the real world by injecting unseen real-time data into your trained machine. This testing will suggest if machine requires any tuning


Regulate/Tune up parameters

Post evaluation, the next step is to check the efficiency of the dataset you are using to train the model. Make sure to insert your database to the selected model many times to escalate model accuracy.

Cutting-Edge ML Mobile Applications in 2021

Modern machine learning algorithms are bringing new cutting-edge applications to the market, thus modifying the way in which users interact. The top applications in this list are as follows:

Tinder

Tinder utilizes ML algorithms to discover a specific match. The algorithm analyzes information like posts, pictures, percentage of user likes, swipes on an image, etc. The most swiped photo, for example, is presented as foremost to that specific user by an algorithm. The scheme employed raises the chances of an ideal match for users.

A first person view of someone kicking back with their feet up waiting for Nexflix to load.
Photo by Mollie Sivaram on Unsplash

Netflix

Netflix has saved around $1 billion via their recommendation system because 80% of their TV shows are suggested by this system. Explicit and implicit data is the basis behind these recommendations. The ML algorithms of Netflix (linear regression, logistic regression, etc.) are trained by user reviews, ratings, user search requests, and behavior. Algorithms get acquainted with this behavior over time and offer filtered content.

Snapchat

Snapchat using supervised machine learning algorithms to simulate computer vision. These face tracking algorithms detect human faces to build elements (glasses, beauty filters, dog faces, objects, etc.) and transform the picture's texture.

Google Maps

Google Maps trains its ML models using geo-data collected from user’s activity. Through this data analysis technique, Google Maps predicts parking slots.

When the user's location is on, the researchers fetch and cluster monitoring data to train multiple models.

Facebook

Facebook's machine learning algorithm interprets a person’s profile, interests, friends, and friends of friends. Through this evaluation, Facebook suggests profiles in the section “People You May Know” according to your interests.

Newsfeed, Facebook ads, and facial recognition are some of the features where Facebook applies machine learning.

An open laptop on a table with Spotify running on it.
Photo by sgcdesignco on Unsplash

Spotify

Spotify's machine learning model works in three stages.

  • Collaborative filtering is the first one in which users are provided with recommendations on a personalized lists of songs. This recommendation is made through a comparison of multiple playlists created by users.
  • The second one uses a natural language processing scheme to interpret lyrics, read blog posts, and discussion about trendy musicians and articles. This way, the algorithm categorizes its top terms and suggestions.
  • The audio model comes at the third stage where algorithms evaluate data from audio songs and make suggestions based on identical music.

eBay Bot

eBay leverages the reinforcement algorithms of ML to hand out their best feature, “ShopBot”. This bot understands and processes the user’s text messages to recognize what the user wants.

eBay’s chatBot is popular because of its user-friendly conversation and flawless understanding of context.

Other Applications of Machine Learning

E-Commerce

Popular apps like Amazon, eBay, and AliExpress use ML methodologies to detect fraud, rank, understand and expand products in multiple categories, analyze forecasts and promotions, and learn user behavior.

Health

Facial recognition is a great technology for health and fitness mobile applications that detect diseases and maintain secure data for each patient via ML algorithms.

Financial Assistants

VAs/chatbot have remarkable business applications, such as handling repetitive tasks and answering FAQs about products.

Closing Thoughts

Machine learning in mobile app development is a game-changer for this generation. Artificial intelligence and machine learning are powering the future of innovation and creating meaningful experiences for app users.

According to a business's scope and requirements, best-fit available machine learning models can be employed to fuel innovation and save money. If you have a need for machine learning applications, Crowdbotics offers expert PMs and developers who can bring your project to life. Get in touch with our team today for a detailed quote and project timeline!