integrating machine learning in mobile app

Machine learning is definitely changing the world gradually. The machines that are used by humans for their day to day use are getting smarter. This includes our smartphones, smart wearable or even smart televisions. These smart devices are capable enough to take decisions on their own. Thus, have you ever noticed how google map notifies you about the time for office and the time for coming back home. Obviously, you have never asked Google to keep a track of it. But still it suggests based on its capability, and this capability is built through machine learning technology.

Mobile phones, especially smartphones play an important part in our life. Today, not only phones are used for communication, but it is also used for day to day work. Hence, just imagine if the user’s payment app learns on which day of the month you pay your telephone bill, then in the next month user will get an automated reminder or maybe the app itself can pay the bill if the user forgets. This just enhances the user experience, as the mobile platform become much smarter and more intelligent. Use of Machine learning is redesigning the mobile platform.

How does machine learning work?

In simpler terms, in ML (Machine Learning) the machine trains itself rather than being explicitly programmed. ML is an application of the AI (Artificial Intelligence), through which the system learns automatically and gradually it improves itself with experience. All this happens without any explicit programming of the system or machines. ML specifically works on the development of programs or applications, that can train themselves from the data. Technically, this is also referred to as training from the dataset.

For example, you have developed an app for maintaining monthly grocery list. The app will learn gradually from the input provided by the user. Ultimately, it will identify the patterns from the data, and train themselves. Then at a later point of time app will start recommending items for groceries based on the previous user data. This approach will get more refined, with usage.

The primary aim of ML is to allow the applications to learn automatically, without any form of human intervention or human assistance. As the application keeps on learning from experience, it develops decision-making capability, and ultimately get transformed into an intelligent application or a smart application. Because of its unique characteristic machine learning as technology is becoming popular amongst the mobile app developers as well as mobile companies.

How Machine Learning can be used?

Lets have a look at some real life cases on how machine learning in mobile apps can be used

  • Personalization: ML could be used for developing a personalized experience for app users. Through it, the app can learn about various information about the user, based on a pre-determined algorithm. It can learn, right from the user’s social media usage to finance payments. The application trains itself from multiple sources of data. The more it trains from the user data, the more knowledgeable it becomes about the user.

    With it, you can profile as well as structure users individually, and create a personalized approach for each type of customers. It helps to create more personalized content as per user behavior. At one of the time users feel that the app is their personal assistant and it is interacting them as an intelligent human will do. This experience becomes much deeper as the machine keeps on training itself with the user data. A good example will be the modern-day music apps. It actually tracks the user behavior by collecting the user data, and at one of the point, it starts providing personalized recommendations to the user, just like a smart personal assistant. So, probably the app has learned that the user listens to a particular genre at night, it will start recommending track based on that preference.

    ML also aids in the future development of the app. It can determine who are the target users of the app, what are the requirements of the users, what criteria they are using to search for your app, and many other user preferences.

  • Advanced Search: ML allows search optimization within the application. It creates a better user experience by providing better as well as relevant results. The search becomes more intuitive and result oriented. Using this, the app continuously learns from the previous customer queries and based on that the results are prioritized & customized for the user. Based on the search history and user behavior, app refines the search for the user. This is very common with the shopping apps, it keeps track of the user search history, and based on that it shows refined search results. For example, a user is searching for polo tees with any colors. However, previously user has searched for different apparels in blue and grey colors. So, in this case, the search will be optimized by the app, and it will show the blue as well as grey polo tees in higher ranks, followed by the other color tees.

  • Predictive Analysis: Predictive analysis makes use of historical data and develops a model with the help of machine learning and artificial intelligence. The historical data is used as input for the development of the model and identifies key patterns as well as trends within the data. This model can predict, based on its learning from the historical data.

    Machine language can facilitate predictive analysis for different types of mobile application. For example, a customer’s shopping pattern could be predicted, and he can get automated product recommendations based on his shopping pattern. The predictive analysis could be used effectively for detecting fraudulent transactions. It can also predict customer segment for a particular product group. This is a feature used for marketing campaigns and has been quite effective in generating leads.

  • Filtering and Security: As the app learns about the user through ML, it becomes aware of user preferences. Hence, based on the user preferences it starts filtering out the content for the user. So, the user gets only what he wants.

    ML also optimizes the authentication for the app. It recognizes the voice, video, and audio, which can be effectively used for biometric authentication. This includes authentication techniques like face detection or fingerprint scanning. It also ensures access rights for the users. With these security features, machine learning can easily detect an intruder from a different IP address or physically exploring the device, as biometric data won’t match.

  • Relevant Advertisements: For any advertisement campaign, it is important to show the right kind of Ad to potential customers. In simpler words, this means personalized advertising. Through the implementation of machine learning your app can not only track the user preferences but also collect the all-important user data. As per a study, around 38 percent of business executives are using data determined through machine learning, for their Ad campaigns. Ads will be dished out to the customers based on their interests and search patterns.

Key apps using machine learning

Today there are many apps with machine learning features integrated. This ranges from intelligent assistants to chatbots, to a different type of utility apps. These apps have made the mobile platform smarter and intelligent than before. Lets have a look at some of the examples:

Prisma: Prisma is a photo editing app. It makes use of artificial intelligence and neural network to create a more artistic effect on a particular image. It perfectly uses AI in combination with a neural network to mix the famous art styles (like Pablo Picasso & Vincent Van Gogh) with the input image. It makes use of different filters to bring the desired effect. Hence, it is different from general photo editing apps, which simply adds filters and Color to enhance image quality.

Snapchat: Snapchat is a messaging app. This app is an infusion of augmented reality and machine learning, that creates computer vision. It makes use of facial tracking algorithm, in order to identify the face from the snaps.

Tinder: Tinder is a matchmaking app. It is famously known for finding soulmates. Tinder makes use of a technology called “Smart Photos” that is used for effective matchmaking. Smart Photos is based on machine learning. It makes use of ML to analyze the photos of the user, in order to determine the frequency of swipes (right swipes or left swipes), ultimately determine the most right-swiped photos.

Does your app need machine learning?

This entirely depends on the purpose of your app. It depends on what kind of problems your app is going to solve. So, you need to analyze, whether those problems can be solved by machine learning or not. Not necessarily machine learning should be used for only problem-solving. It could be also used for adding more value to the app. For example, you have designed a food ordering app. The app automatically recommends the restaurants based on your location. At a later stage, it could also recommend restaurants based on the restaurant’s profile, from which user order’s frequently. If machine learning was not implemented, then the user has to manually set the location and then he has to execute the search. Thus, the implementation of machine learning adds more convenience to users.

Top 5 platforms for developing a mobile app with machine learning has been developed by Google, and is used for development of business solutions for personal assistants on both, Android and iOS. is based on natural language conversations, which ensures that the personal assistants created through answer user questions in natural language. The functionality of is characterized by the functionality of a tool that converts a speech into a printed text. This is one of the best platforms for creating intelligent chatbots. is available on all the three mobile platforms, Windows, Android, and iOS.

Tensorflow: Tensorflow is an opensource AI library. It uses data flow graphs to build different models, that could be used for implementing different tasks. Tensorflow is a reliable platform for the development of complex machine learning applications, as it allows the creation of large-scale neural networks with different layers.

IBM Watson: Watson has been developed by IBM. It is a question answering computer system, which is capable of answering any questions that have been posed in natural language. Watson is also known for faster processing of data with the help of integrated approaches. With AI capability, Watson can solve complex problems, that differentiates it with others.

Azure: Azure is a cloud platform. With the help of its AI enabled analytical mechanisms, it is capable of making accurate forecasts.


A mobile app equipped with ML and AI is guaranteed to be successful in today’s app market. As a developer, you can refine your app, make it smarter and more intelligent by using machine learning. If you are looking for personalized experience, predictive analysis, and advanced search, then machine learning platform is meant for you. Moreover, you can create robust machine learning apps, without compromising with the project quality, and project deadline.