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Machine Learning in Mobile Apps: The Next Wave of Enterprise Mobility

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Last updated on  
February 17, 2023

Artificial Intelligence (AI) and machine learning (ML) have changed the mobile app development process. The mobile app developer depends on intellectual technology like ML to write powerful algorithms to create great apps that can capture and understand the human mood and behaviour. Get ready and have a cup of coffee before we proceed. Let’s dive into such breathtaking technologies used machine learning in mobile apps development.

Social media platforms are experiencing tremendous growth in recent years. The mobile app’s power, productivity, and value must exceed the buyer’s expectation to be a successful product. Indeed, this trend overlays the need for more advanced mobile apps, which can provide delicate performance without requiring too much space or battery.

By the end of 2020, the advanced mobile apps will extend to multiple sectors. VR and AR apps will observe a massive growth that could hit an $18.8 billion by the end of this year. There has been an approx. 300% increase in AI and ML investments. The big players, including Google, eBay, Microsoft, IBM, Facebook, and many other biggies.

Another crucial point, after 2018, more than 70% of business entrepreneurs increased their investment in mobile apps for both Android and iOS. According to the market watchers, these apps’ market value will peak to the US $430 billion in 2021.

Tech and Mobile App Development: Two Sides of the Same Technology

AR and VR play an essential role in app development. Thousands of people used AR and VR for games. By 2025, the growth expectation is $191 billion. The manufacturing and health sectors are the primary areas where AR and VR powered mobile apps will become massive hits.

Security is one of the significant factors that play in the success of an app. Because nowadays, audiences are more concerned about privacy. There are various reasons why security is becoming a prime factor, and blockchain usage helps a mobile app developer improve tracking and security. The growth of blockchain permits the development of more sophisticated payment and money transfer apps.

Next to security, storage is of significant concern. Mobile manufacturers try to introduce new models with more internal storage options, yet users find it insufficient. The use of iCloud allows apps to utilize less space. Apps with massive databases depend on AWS or similar services for backup. Such technology overlays the way for enhanced collaboration and improved productivity in the future.

Lastly, 5G (Fifth generation network) technology, everyone is waiting for it. It helps to transfer data a hundred times faster than today’s 4G network. Such a faster network will enhance the development of ultra-performing apps, which would make a twirl in healthcare. The combination of 5G and VR would bring new upcoming 3D gaming apps and healthcare applications.

How Has Machine Learning Technology Restored the Usage of Mobile Apps?

The Artificial Intelligence subfield is getting recognition for constant, data-dependent learning. It uses actual analytics to spot behavior, differences, and regularities. ML is having a considerable impact on the development of the latest machine learning in mobile apps. The acceleration from ML-powered computer applications towards smartphone apps has been rapid and highly productive. And overall, it becomes a profitable option for developers.

With this in mind, ML apps revamp smartphone usage with the following effectual approaches:

Modern mobile users expect simplicity, convenience, and functionality; Through machine learning apps, they can record hobbies, manage work, manage calendars, notify events, and recommend solutions.

Searchers want relevant and informative results. ML tools are useful in tracking such actions and analyzing historical data. These tools correct spelling, respond and react to voice searches, and offer lists of your expected outcomes.

Business leaders expect top-of-the-shelf app functions. ML algorithms help with metrics and intelligence gathering. They facilitate data accuracy, decision-making, personalized services, and secure connectivity.

ML-powered mobile experiences are faster, smoother, and quickly responsive. Their practical approach is suitable for high-speed latest smart devices. Machine learning in mobile apps helps developers build powerful apps through data filtration, algorithmic training, model selection, and predictions.

An upsurge of ML-based Mobile Apps

AI and ML technologies will bring more spectacular mobile apps. As per trade pundits, the investments in ML will reach US$58 billion by 2021. The industry’s growth will peak at US$20.83 billion, with a CAGR of 44.06% by 2024.

Note- The estimation might change later as per the situation.

Machine learning applications will invigorate the eCommerce industry. Security, Photo, and Video apps will grow by leaps and bounds. To keep it different, IoT devices are smartphone-controlled, so the market potential for ML-powered mobile apps is also enormous.

App using machine learning

Without noticeable results, investors will not continue their benefaction. Thankfully, multiple applications satisfy their trust. These various ML-powered mobile apps are robust and user friendly. Some of the finest ML-powered mobile apps are:

1. Snapchat

One of the famous apps amongst all. This messaging app has one of the best face detection capabilities. Its filters combine Machine Learning algorithms with AR. Photos get processed as data sets with individual color pixels. Repeated scanning and grayscale values get processed to identify various faces. To emphasize, it also creates 3D masks using data training, rotation, alignment, and much more.

2. Google/Apple Maps

Google/ Apple studies and analyzes thousands of data sets. They use training models for cumulative location data. And later, logical regression is applied to make parking lot predictions and related services.

3. Tinder

One of the well-known dating apps uses Smart Photos to find perfect matches. Profile swipes get analyzed to categorize popular photos. The system keeps on learning and improving itself using inputs.

4. Netflix

This streaming service uses deterioration and learning algorithms too. It differentiates content based on genre, actors, reviews, etc. The app learns from user action, choice, search, and behavior to recommend the movie as per your choice.

Why Use Machine Learning Technology For Your Mobile Apps?

Machine learning started as a pattern recognition program, and today, the programmers train the modules to conduct a particular task. ML can help to read big data and generate big-picture analysis within seconds. It is quite possible to adapt the ML module to accept new input and self-learn to create a continuum of information. Crucial information is the key to the growth of any business. The first concept of a self-driving car is possible only with machine learning and AI. Machine learning app can help to monitor, create, and understand.

Advanced ML algorithms analyze information from your social media profiles. They facilitate a learning process using digital units. To put it differently, users who use apps can experience a personalized experience. They will receive recommendations in their social media app immediately.

Increase user engagement

Machine learning apps have the capability to increase customer engagement. Customer engagement and experience can be fulfilled through the function of information categorization. It is possible to convey the app’s real intent with approached buyers.

Visual verification

ML methods include facial recognition. Developers can write apps with auto-recognition. ML permits account access and a secure authentication process. M-commerce apps have become more reliable, expedient, and user-friendly.

Users behavior

Consumer’s online behavior and interests are valuable. ML algorithms feat these preferences to assess customer’s attitudes. They use insights to enhance advertising strategies and lower and upper sales pipes to achieve higher profits. Location, gender, and app usage data analysis is possible with ML methods.

Online security

Mobile apps help rationalize and secure audiovisual data. Voice recognition, biometrics, and face recognition to improve security. Banking and financial sectors benefit from selfie-style account access. It prevents identity theft and stop breaches in business and personal data security. Automated product tags and insightful business intelligence are some of the other benefits.


Concluding with the discussion, the next-gen mobile apps will be smarter and will be way ahead of your imagination. Developers will employ the finest machine learning techniques. They apply predictive analytics, data mining, biometric, and facial recognition techniques and rely on neural networks. The outstanding ML processes, including mining, tracking, analyzing, searching, and predictions, benefit sectors like health, finance, education, and entertainment. The next-generation mobile apps will have advanced security, search, predictive, and customization features. And their UI/UX will be sharper, authentic, and entertaining. Are you planning to create a new mobile app for your new startup? Try to make a profitable and self-learning app with ML techniques.

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