How to Use Machine Learning in Mobile App?

The technology that promises to bring massive changes to the world in the next few years is machine learning (ML). Machine learning is a subfield of Artificial Intelligence research and got the highest spotlight in business.

ML represents a new era in software development where computers, gadgets, and other devices don't require special programming to complete tasks anymore. Instead, they can collect and analyze information that is needed to draw appropriate conclusions and learn during program performance.

Now machines can accumulate previous experience to make decisions as it occurs among human beings. Of course, the process of learning requires special algorithms that would “teach” machines. That is why, at The App Solutions, we use machine learning in mobile app development.

To understand the scale of the ML industry, let's take a general outlook on the Artificial Intelligence market. According to Bank of America Merrill Lynch, over the next five years, the market will extend to $153 billion compared to $58 billion in 2014.

Venture Scanning gives an infographic that summarizes the Artificial Intelligence market and shows funding of every category. The chart shows that the ML applications category is leading with over $2 billion market share, which is three times more than the total funding of the next Natural Learning Processing group.

ML has started from the computer, but the emerging trend shows that machine learning mobile app development is the next big thing. The modern mobile devices show the high productive capacity level that is enough to perform appropriate tasks to the same degree as traditional computers do. Also, there are some signals from global corporations that confirm this assumption:

  • Google launched software that uses neural networks and provides language translation. Technology is optimized for smartphones and works without any internet connection.
  • Lenovo is working on its new smartphone that also performs without an Internet connection, executes indoor geolocation and augmented reality. All this is possible with high-speed image processing software, as well as Google software.
  • At the last WWDC 16, Apple has presented Siri SDK, and now all developers can incorporate this feature into their apps.

See also: Aplications of artificial neural networks

Machine learning application areas

Machine learning is a very multidisciplinary field and can find its implementation at the intersection of technologies, science, and business.

Application of machine learning in robotics

If one takes a look at the robotics industry, engineering includes not only mechanisms but also cognitive technologies. Today we are witnessing the emergence of the era when robots assist people on work and household, take care and entertain them. And people will manage these machines with voice commands or program tool actions with only a few taps on their smartphone's screen. All it needs is a machine learning feature for performance in an unpredictable environment.

Implementation of machine learning in data mining

The field of data mining serves to analyze big data and to discover exciting, non-obvious connections within a significant set of data. It consists of data storage, maintenance, and actual analysis. Here ML provides both a set of tools and the learning algorithm to discover all possible relationships. Further, in this article, we will talk about how to use this technology for predictive analytics when you need to develop a mobile app with machine learning for eCommerce.

Application of machine learning in finance

In the finance sphere, machine learning algorithms are widely used for predicting future trends, bubbles, and crashes. For example, custom software can analyze all types of information about borrowers such as a history of previous transactions and social media activities to determine the credit rating. Or the system can bring an outcome considering portfolio optimization and send recommendations right to the smartphone.

ECommerce realization of machine learning 

For eCommerce machine learning also opens new opportunities for revenue and improved customer experience. Such retail giants as eBay and Amazon already proved it. But these tools are available for smaller players as well. At The App Solutions, we provide eCommerce machine learning applications for our customers. These apps can be completely custom or with the usage of open-source APIs and SDKs (Amazon ML API, Google Cloud Prediction API, etc.).

Enterprises can use ML algorithms to their advantage in entirely different aspects of their business.

READ ALSO: Do You Need a Mobile eCommerce App for Your Business?

Product Search

To give users relevant information according to their pursuit of the eCommerce app, our developers implement the whole set of ML tools such as ranking, query understanding, and expansion related questions and so on.

For instance, for product ranking, we use customer information about the click-through rate or product sell-through rate. Additionally, we analyze behavioral data during searching and the purchase process. Drawing on this, we create graphs between different goods and queries.

Another interesting tool is to query intent detection. It comes from understanding the user's portrait, his search history, and semantics outcome.

Product Recommendation and Promotions

The recommendation system is built on the collaborating filtering method. The App Solutions team together with our partner Softcube provide clients with significant data service for smart recommendations and digital merchandising (“this item fits…”).

The system is built upon the site content analysis, user behavior or purchase patterns, and even upon the business logic of the enterprise. Predictive analytics makes the challenge more comfortable, and recommendations become even more relevant with time.

Such technology gives up to 7-12% of the same traffic.

Trend forecasting and analytics

The eCommerce enterprises, especially those, working in the fashion industry, always have a lack of information to understand and quickly respond to the latest trends. They have information about past season sales and upcoming tendencies. But between these two sources, there is a huge gap of missing opportunities.

Big data ML allows aggregating the trends and sales information from different open sources (inspirational blogs, social media, designer reports) and give predictions in real time.

The same issue could be implemented in price management.

Fraud detection and prevention

One way or other, every eCommerce company has faced this challenge. The annual ad fraud costs reached the point of $32 billion which is 38% more than the year before.

Machine learning plays a critical role in building a defense system. It involves the ongoing monitoring of online activities and triggering of alarms.

Here is the general workflow of “abnormal” behavior patterns detection:

However, the number of opportunities goes far beyond the list. Our team also provides the following machine learning for eCommerce app solutions for our business and startup clients:

  • Image recognition;
  • Product tagging automation;
  • Shipping cost estimation;
  • Logistic optimization and supply chain management;
  • Wallet management;
  • Business Intelligence.

7 successful machine learning tips for developers of the apps

  1. The more data is provided to the algorithm, the more accurate are the result and predictions. It means that the machine learning engineer should avoid subsampling and use all available data.
  2. The critical secret that determines the success of the project is to select the most appropriate ML method. Also, the simpler the model, the easier the learning process, and the more accurate the predictions.
  3. Bring the data scientist to the project to choose the right method and parameters for the best results.
  4. The data is the basis of machine learning. Improper data collection can hurt prediction capabilities.
  5. Understanding data features and its improvements also have a high impact on succeeding learning processes and predictability.
  6. It is essential to take into consideration the business model and production capacities of the client while building machine learning algorithms.
  7. Machine learning algorithms require proper and careful testing, which should be taken into account when planning the costs and timing of projects.


For now, we are just at the beginning of the formation of machine learning technology. Unlike other areas of artificial intelligence, machine learning has become a reality that we can feel and estimate all facilities of.

Developing a mobile application for eCommerce business and providing it with ML algorithms, you get ahead in the growing industry. Shoppers are spending more and more time online on their mobile devices and expect their shopping experience to get more and more personalized and comfy.

Technologies unlock new potential for market leadership with improving every aspect of commercial workflow.

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Volodymyr Bilyk

Content Manager

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