Habilelabs-Logo
Blog

Machine Learning Coming to the Aid of Mobile Applications

August 5th, 2022 . 7 minutes read
Blog featured image

Disruptive technologies like Machine Learning (ML) are revolutionizing mobile applications. ML-based mobile apps offer robust features to add to the convenience of users.

Moreover, mobile experiences powered by machine learning are seamless and more intuitive. Thus, mobile app developers increasingly rely on ML to develop ground-breaking mobile apps. This article will shed light on the use of ML in mobile app development. 

The world we live in today is driven by technology, with machines capable of learning human behaviour and improving our daily lives.

Nowadays, almost every individual owns a smartphone loaded with different apps. Since mobile apps are becoming an integral part of our everyday life, IT companies are leveraging advanced technologies such as ML to simplify our fast-paced lives.

Let’s dive further into the details to learn about the uses and advantages of ML in building mobile applications-

Uses of Machine Learning in Mobile Applications

The use of ML in mobile apps has become almost ubiquitous in recent years. 

Some popular ML-based apps we use daily are Snapchat, Google Maps, Netflix, Tinder, JioSaavn, Gaana.com, and so on. Ride-sharing apps like Uber, for example, display the location of our hired cabs/bikes in real-time.

Similarly, food delivery apps show a list of restaurants that deliver our favorite cuisine. The apps we use daily can do anything from ordering food to managing our time to reminding us of our daily schedules.

Moving further-

  • ML for mobile apps enhances security and weeds out malicious data.
  • Mobile apps integrate machine learning for predictive analysis. It helps process vast volumes of data for predicting human behaviour.
  • Optical character recognition (OCR) apps use ML to remember and recognize characters that developers overlooked.

Now let’s talk about how ML can be advantageous for mobile app development.

Predict User Preferences

ML-enabled mobile applications are a boon for marketing professionals. Data related to gender, age, geographic location, browsing history, and other characteristics of users generated by ML helps marketers better understand their preferences.

Additionally, data generated by ML also helps them enhance the efficacy of the mobile app. For instance, OTT applications use ML to create custom recommendations based on an individual’s preferences.

Offers Personalized Experience

With ML algorithms connected to any mobile app, your device can receive recommendations based on your social media activities.

The algorithms help you identify your customers, their likes and dislikes, and their perspectives on your products and services, among other things. You can use this data to classify customer behaviour.

Also, you can utilize that information for target marketing purposes. In simple words, you can use ML to deliver personalized experiences by creating more relevant and entertaining content for your existing and prospective customers, thus boosting conversion, customer engagement, margin, and ROI and reinforcing your brand’s image.

Helps Create Relevant Ads

The 21st-century customers prefer high personalized experience. By integrating ML into your mobile application, you can attract your target customers by generating ads that cater to their preferences instead of wasting money on creating irrelevant ads that do not appeal to their tastes.

Enhanced Security

Mobile apps can also be secured and authenticated more easily with the help of ML technology. ML algorithms can identify and prohibit fraudulent activities.

Furthermore, ML can enhance the setting up of biometric data on mobile devices with advanced security features such as audio/image recognition. Also, ML helps prevent identity theft and assists you in granting access rights to your customers.

Agility

By using machine learning, you can make your mobile apps more agile and relevant in a world where trends are constantly changing. ML-powered apps are also more efficient because they cater to users’ needs more efficiently. This is the beginning of a new future for IT and apps.

Profitable & Cost-effective

Artificial Intelligence and Predictive Analytics are revenue-generating technologies that rely on machine learning, making ML-based apps more lucrative.

ML-powered mobile apps don’t have to account for every possibility, they are more cost-efficient. 

Intelligent Results

ML-based apps are designed to track, comprehend, predict and respond intelligently. For instance, the Gmail interface prompts you to attach a document when you mention an attachment in the body of the mail but forget to attach it.

These are the game-changing benefits of using ML in mobile app development. Since ML algorithms for mobile applications offer such significant benefits, they will likely become the new standard for many app types in the near future.

Best Platforms for Creating ML-powered Mobile Apps

Now that we know the advantages of using machine learning in mobile app development, let’s have a look at the best platforms for creating smart and super apps with ML:

Azure

This platform lets you build and deploy ML-based models with accurate prediction capabilities faster than ever.

The Microsoft cloud solution provides a comprehensive support network and a vast collection of accessible tutorials and multilingual documents.

API.ai

This platform by Google is ideal for developing AI-enabled virtual assistants for iOS and Android devices. The Google development team has created a compelling database for API.ai that strengthens the platform’s algorithms.

IBM Watson

Mobile app developers and data scientists can work together to quickly and effectively incorporate predictive accuracy into their apps using IBM Watson Machine Learning.

It enables developers to execute user requests thoroughly irrespective of format. This innovative platform can instantly analyze any data type, such as audio content, images, or textual files, using a variety of methodologies.

Tensorflow

Google’s open-source library Tensorflow works by tracking user interaction in their surroundings and eventually determining the accurate response depending on the requests. This platform is not ideal for novice developers. 

Wit.ai

This platform is quite similar to API.ai. Wit.ai’s most notable feature is turning audio files into textual documents.

It also offers “history” features, which examine context-specific data to provide highly accurate responses, especially when it comes to commercial chatbots.

Challenges of Building Mobile Apps with Machine Learning

Certain challenges are associated with Machine Learning, as with any other technology. The basis of machine learning is the availability of adequate training data. Therefore, the volume of training sample data must be large enough to guarantee fundamental machine learning accuracy.

The following ML processes can help you avoid misinterpretations of visual cues:

Data Augmentation

This technique is an effective tool for overcoming the obstacles that developers experience when developing ML-based apps.

Data augmentation can help improve the performance and results of machine learning models by adding various instances to training datasets. It generates rich and adequate datasets to enhance the performance and accuracy of a machine learning model.

For example, in cases where a mobile app is asked to identify a central image, data augmentation alters the image without affecting the subject to enable the app to detect the object accurately in different environments.

Hard Sample Mining

A hard sample is one in which your machine learning model struggles to predict the labelling correctly.

If the sample size for analysis is insufficient, the machine ought to confuse the identical items with a primary object if there are multiple similar items. The machine learns to identify the central object by comparing different objects with numerous examples.

Hard sample mining converts raw unlabeled data into a smaller amount of high-quality labelled data, enabling ML to predict labels correctly.

Data Addition Imitation

A few data points are nullified in this method, leaving only the central object’s information. Consequently, as a result, the machine memory can only store images related to the main subject image instead of images related to other objects surrounding it.

Wrapping Up

Having a brilliant mobile app concept and design is one thing, but incorporating machine learning may elevate your users’ experience to the next level. Besides, ML-based apps can drive innovation across industries by providing the necessary functionality and content.

Given the potential of ML technology, it seems reasonable to assume that mobile apps can benefit significantly from machine learning because it allows for good personalized options to improve efficiency, effectiveness, and usability.

Author: Habilelabs
Share: