How Machine Learning helps in Mobile App Development

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The article hopes to help you understand all that you need to know about the market trends, steps to build an ML-based app, its benefits, and even a few examples of already existing such apps.

Given the many benefits of Machine Learning, it has taken over almost all sectors. App development is not unknown to this either. So, if you are looking to build an industry-specific app that integrates machine learning, you have found the right place.

Have you ever wondered how Facebook knew who ‘You might know’ or how Netflix knew what ‘you might like’? The answer to this is Machine Learning. With the progress made in technology Machine Learning (ML) can understand user needs based on user behavior, buying/browsing patterns, and many such aspects, which makes it easy to make any service customer-oriented. What ML does is learns how to classify data by improving with each classification and fine-tuning itself to provide more and more accurate results with each try. Using ML algorithms companies create systems that extract user requirements and provide them exactly what they are looking for to guide them or even for entertainment purposes. 

ML enables electronic gadgets to efficiently provide user-specific functionality without any special programming needed for each user. The gadgets gather data continually and based on that comprehend user behavior to make user experience good.

In mobile apps, ML can aid boost businesses by steadily learning various user’s patterns and providing them with a very accurate suggestion in what they might be looking for.

ML is not used only for assisting the user with services, it can also be used to understand what the user would like for the design of the app and other such features. To gather information, the system uses various social media platform activities.

So, what is it about ML that will help your app?

Incorporating Machine Learning can prove to be fruitful in many ways as far as a business is considered. Here are a few of them:

  1. Statistically, using ML in apps recorded a rise in sales for 76% of businesses.
  2. ML optimizes search results by showing the user such results that seem more relevant to them than others.
  3. Helps in product innovation as it knows what all of its users are looking for and can thus help the business deliver exactly what their customers want from them.
  4. Provide assistance navigating through the app using chatbots and resolve queries if any arise.

Which apps already use it?

The most common apps that use ML are: 

  1. Facebook: It uses ML algorithms to identify people that may know each other and makes suggestions to all user. It also makes use of ML for facial recognition such that when you upload a photo and want to tag someone in a picture, the app gives a list of friends that it thinks might be the people in the picture.
  2. Spotify: This is another very commonly used app worldwide the implements ML in its application. It can be seen in the ‘Made for You’ tab or the ‘Based on your recent listening’ tab. What the system does is extracts the genre, artist, album, etc. that you frequently visit and curate personalized playlists for you to make your listening experience exceptional.
  3. Snapchat: The area of ML used by Snapchat is facial recognition when using filters. It uses a supervised learning algorithm to identify the facial features and add objects such as a crown or dog ears. It even detects facial movement such a lip movement, when you are asked to open your mouth for a few filters or raise your eyebrows.

Steps to building an ML-incorporated mobile app

The entire procedure only needs integration of such algorithms that can understand user requirements efficiently, the rest is the procedure of designing an app like you normally would. So, integrating ML in an app goes through the following stages:

  1. Collect and clean the data

To be able to predict user requirements efficiently, the system first needs some data to understand how users operate. So, in this, we collect all possible data needed for prediction and filter out the inessential data like the duplicates, errors, missing values, etc. After data is gathered, it is randomly arranged to eliminate any pattern formation and split the data into the appropriate proportion for training and testing based on your application. 

  1. Select and train the model

There are a wide variety of ML models available that can be chosen for various applications. In this stage, you need to identify what is the end goal and select an appropriate model to suit your application. 

When training an ML model, you need to pay extra attention to the fine details. For example, your application is a recommender system, you need to make sure all the aspects that will help prediction are well-defined. You will also need to minimize errors especially in those fields that will majorly affect the prediction. The system needs to ensure that no entry has a missing value for domains that help understand user behavior. The dataset used for training needs to be clean and large enough for the model to recognize what exactly is it looking for.

  1. Evaluate the model

Once the model has been trained, use the testing dataset to make predictions based on the model that has been developed. It is in this that you will understand how the model performs when it is working in the real world.

  1. Parameter tuning

When the model is tested in the previous step, you will understand the areas where it is not working as efficiently as you expected it to. So, in this step, you retrieve all the system errors and correct them by fine-tuning your parameters to focus exactly where you need your system. Repeat this step till you finally get what you were looking for.

  1. Perform predictions

Now, your system is ready to be incorporated into a mobile app and be sent into the real world to be deployed. The user implements the system to make predictions and get services from the app personalized for their choices.

Now that we know how it is built, let’s understand how much it costs to build an ML-based app

When building an app, this is one of the major factors that influence the choices and so is the case for ML-based apps. So, an application development company charges you according to the development hours for that particular app. Similarly, incorporating ML features in your app works on the same principle. Thus, more features the app expects higher the cost will be and eventually more efficient. As every feature, you incorporate in the app requires at least some designing, project management, requirement analysis, programming, testing, and debugging thereby increasing development hours leading to higher cost of development.

Application areas for ML apps

Due to its versatility, machine learning in application development has widespread applications as follows:

  1. E-commerce: Online retailers use ML in multiple ways in their applications. Such as, for making suggestions to the customer based on what they usually buy or what they have been searching for. The system even makes personalized deals for selective users based on their purchases from the app. It can be used to help prevent identity fraud or credit card fraud using visual search. ML can also be used by e-commerce companies to help provide easy customer support by using AI Chatbots.
  2. Fitness mobile apps: These apps understand the type of body its users have, the kind of nutrition they prefer, the lifestyle they live, track data from devices such as smartwatches, and fitness trackers. Then, based on what the goal of the user is, the system curates a personalized diet program, a personalized workout program, and even suggests reminders and tracking each activity.
  3. Transport apps: Delivery service providers and other such logistics apps, like Uber, need to keep the driver updated with all traffic conditions and also provide with such a route that it minimizes travel time, avoids traffic jams, and reduces fuel consumption. For this purpose, the system is built with a road optimization integrated with machine learning. The system even predicts traffic conditions for any given time and date based on all historical data. The system even optimizes ride allocation to appropriate driver based on their locations and the driver’s waitlist, or the shift timings.
  4. Financial assistants: Such apps are usually deployed by banks to help their clients analyze their transaction history, give financial advice, track spending patterns, and predict expenditure using ML algorithms. These apps can be used to gain some insight into personal finances. 

Conclusion

With the rise in digitization in all walks of life, the mobile app development market is steadily rising and is expected to advance even further.

In 2018, the recorder market size value was $106.27 billion which is forecasted to take a hit to $407.31 billion by 2026 which not only tells us where this market is headed but also serves as a motivation to familiarize ourselves with it very well.

However, one cannot ignore the steady network connection required for most apps that is not readily available in developing and undeveloped nations which might be looked at as a drawback for app development.

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