how to use machine learning in mobile app development

Machine Learning in mobile app development undoubtedly has impacted the outcomes, i.e., the mobile apps  in a positive way – where some incredible transformation had been witnessed over the past few years. Machine Learning (ML) empowered with Artificial Intelligence (AI) has been successful in crafting intelligent and smart solutions that can even understand human behavior using behavioral powerful algorithms. It can achieve so by deploying apps that have the capacity to engage users, interact with them, and deliver an utmost  highly-personalized experience. Thus, as a subfield of artificial intelligence, ML has become a significant part of a growing number of industries including mobile app development. Using algorithms, it enables the computers to find insights as detecting credit card fraud, or optimizing manufacturing processes, even predicting customer purchase behavior and the personal interests of certain Web users. Machine learning app development has helped us – a leading ios app development company in Bangalore to transform the mobile app more intelligently which also signifies that the tasks are completed without any special programming.

How is ML influencing app development today?

As reported by IndGlobal- a premium mobile app development company in Bangalore , the ML Apps category enhances the largest sum in terms of venture funding as compared to any other artificial intelligence categories, e.g. ML Platforms, Smart Robots, Speech and/ or Video Recognition, etc. ML apps are evidently even more popular due to their high productive capacity in modern mobile devices. The main aim of machine learning is however to make a mobile application as user-friendly as possible and one should adhere to the following principles to provide an engaging experience to the users.

1. An individual approach: It implements the simplicity and convenience that an user can expect from an application. In fact, any app using machine learning is more pro to anticipate your wishes and which eventually succeeds in recommending you the most relevant content becomes more favourite.

How Smartphone users benefit from AI

2. The search span should be quick: ML tools often come useful to those who wish to find relevant information. These tools are able to analyze a search history and certain typical actions, voice search and a list of related requests.

3. Optimized e-commerce apps are always appreciated: Machine learning fits this sort of application perfectly. Additionally, if data on sell-through rates, or search history and purchase patterns are available, a user is obviously more likely to get relevant information. The ML algorithm here will simply predict his or her search queries.

4. The more data you analyze, the more the success rate to exemplify users’ expectations. Having enough data about a user, you simply increase the chances of getting ML to work for you immensely and in a better way.

The popularity of machine learning also accounts for the shift in the app development paradigm. Programmers who are capable of writing certain algorithms  rarely are explicit about the expected usage and performance if the output is not so apparent from the input. However, systems based on machine learning techniques enable the latter to adjust to the former. AI techniques that are used to train algorithms boost inference performance as a part of machine learning application development. The developer should consider

  • following a simple method in order to make the machine learning process more effective.
  • prediction accuracy depends completely on data accuracy
  • ML algorithms should be tested

Why is ‘MACHINE LEARNING’ imperial for Mobile Apps?

1. Enhances User’s Engagement 

Machine Learning empowers the app’s real objective, that solves half the purpose of developing the apps. It further has the capacity to improve customer engagement, which is easier with the function of information categorization. 

2. Improves Online Security 

Voice recognition, face recognition, and biometrics are few of the exclusive features that aid in building a robust security system for the app users. It also reduces the risk of theft, breaches etc. Once the access to the account is so secure, it also increases data security to make the app even safer and better. 

3. Helps to identify App User’s Behavior

Knowing the interests and the behavior of the customers can be valuable to the businesses. And Machine Learning algorithms help in figuring out the behaviors and utilizing them for delivering highly customized apps for users. Additionally, ML helps mobile app businesses in improving their advertising stratagems to keep the customer’s content. 

4. Predictive Analysis Feature

Machine Learning is capable of processing huge data and deriving quantifiable calculations that are highly personalized based on preferences of the users. Machine Learning also helps in predictive analysis, thereby allowing the businesses to be more specific in deploying any of the results to the users. 

5. Filtering Out Spam reports

While developing mobile apps, the developers often have the option to train users. It can be even programmed to clean insecure emails and websites, which has the capacity to spam user’s inboxes, leading to certain fraudulent activities that can easily be skipped if the mobile apps are incorporated with Machine Learning. 

About ‘Mobile Apps’ & Current Trends

Any android app development company in Bangalore will aim at building high-quality, scalable, interactive, innovative, informative, and productive apps, that are capable of entertaining or assisting the user. Apps are developed for several devices and platforms, including the wearable devices, smartphones, and digital assistants. What really goes into developing a successful & user-friendly app are the perfect blend of technology and tools that require Kotlin, Swift, ReactJS, Flutter, and other similar technologies that further build highly interactive mobile apps. 

  • Mobile Apps and their Market Potential-

As per recent reports, the worldwide spending on the AR/VR market is expected to hit approximately $18.8 billion by 2021, which states that spending on AR/VR products and services across the globe is going to continue to grow throughout the 2019-2023 forecast period, thereby achieving a five-year CAGR of 77%.

Another crucial point is that after 2018, more than 70% of business entrepreneurs have shown increased interest in their investment in mobile apps for both Android and iOS. As per the market watchers, these mobile apps’ market value will rise to US $430 billion by the end of 2021.”

The market dominators in the mobile app arena are Google, IBM, AOL, Facebook, and Intel, which are continuing to grow in the market of mobile app development. 

  • Machine Learning and Developing Innovative Apps

The mobile app industry has emerged as an enhanced one with the usage of Machine Learning. It helps in minimizing the gap between identifying the user behavior and making a proper use of it. Machine Learning aids in gathering the user’s data and comprehending it. Everything can be identified along with the help of Machine Learning and its tools. When Machine Learning is embedded in the mobile apps, it also allows mobile app developers to deliver personalized apps. App users as well appreciate the idea of customization because it keeps them satisfied and on the app for longer. 

Machine Learning in mobile apps no wonder works remarkably as it is able to track the regular activities of the users, understands them, and thereby delivers personalized results on the app. The ML program first reads, and then re-read the user’s behavior to comprehend it completely and act. This continuous learning in Machine Learning helps in developing an innovative app that offers users the experience that they are expecting from the app. 

  • Impact of Mobile- Machine Learning on businesses

There is no doubt that a number of smart machine learning applications is constantly growing. Although there are several apps that are written with a fixed algorithm and which do not adjust by the data received, it will transform in the near future. Users are constantly looking for intuitive and easy ways to fulfil their needs. Fortunately, ML app development helps in fetching predictions for apps without even execution of custom prediction generation code. Today, AI is not only bringing opportunities for businesses, it is also allowing them to respond to customers’ inquiries more promptly, primarily via mobile devices. For the same reason, Market leaders are currently integrating ML in their products, as the advanced techniques for app development and ML algorithms, in turn, are able to adjust the apps to make them better and more personalized.

Examples of popular mobile applications that incorporated  Machine Learning

The high productive capacity of today’s mobile devices renders them a perfect opportunity for highly automated machine learning apps that respond to received queries along with predicted results in real time. Following are a few applications that have perceived ML as an effective way of having tasks completed and have never repented on the decision:

– Netflix

– Tinder

– Snapchat

– Oval money

– Google Maps

– Dango

-Uber

Some Common Examples of Machine Learning integrated in Mobile Apps

  • Data mining for mobile apps
  • Mobile Finance Applications
  • Healthcare applications
  • Fitness and health tracking apps
  • E-commerce applications

Some checkpoints for Developing successful Mobile Apps by integrating Machine Learning

So, in case you are thinking of developing your very first mobile app, it is of value to use Machine Learning, and here’s how:

Check and try to Utilize the Pre-Built Models

When commencing with your mobile app development it is significant to rely on the pre-built models, as that will not only ease your work but also will help you in getting a long way as there will be hardly any requirement to spend hours finding the dataset, for training, and testing everything for accuracy. 

Also, when you are actually using a pre-built model, the chances of faults are fewer. Besides, it leaves no room for inadequate training or low performing results. Also, when the best is available in the shape of pre-built models, why waste time and effort in again building a new one.

Lay Emphasis on developing Native Mobile Apps:

Though developing Cross-platform mobile apps seems lucrative in the beginning; but it is more likely to bring up issues later. Therefore, if  you are investing in performance-driven apps, you need to rely on certain technologies, like Machine Learning. When you integrate  ML technologies in the mobile app development process, it is always easier to keep the customers’ content simultaneously growing business efficiently. 

Converting Models

You should always invest in a development team that specializes in different model formats. For instance, in case your team has specialization in iOS development, it is crucial to have knowledge regarding conversion of models between formats, as there is a need to integrate different platforms collaboratively in the future.

Being an entrepreneur, you don’t have to worry as there are quality tools that can significantly help you in converting various models into a Core Machine Learning format.

Mobile app development services

Takeaway Message-

Machine Learning and its impact on mobile app development has truly evolved by an exponential rate. There are even expectations that Machine Learning and its tools may become the standard in today’s development world, and IoT development as well. Well, the credit certainly goes to improved security, and reduction in efforts and time, costs, along with improved development process, which has established it as an essential factor for businesses today.

Machine learning algorithms can be considered as a mysterious game changer. They adjust mobile applications to construct meaningful and personalized experiences. These apps also give their users the required functionality and content-driven innovation across every industry. The user and the intelligent system also interact with each other primarily to improve the system’s accuracy. The human-computer collaboration is undoubtedly a promising direction for ML systems to work more intelligently which implies that companies and developers who are still doubtful about its usage and scope, should come forward putting all doubts in rest and try using ML to reap the benefits from it.

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