How to Use Machine Learning in Mobile App?

Have you ever watched the movie recommended by Netflix used masks in Snapchat or found a perfect match on Tinder? If so, you already know what machine learning is used for. Nowadays, this technology has numerous use cases in different fields, such as health care, e-commerce, and, of course, mobile apps. 

If you consider implementing machine learning in your mobile app you are in the right place. In this article, we share machine learning for mobile app usage cases, most successful machine learning app examples, and ML development platforms overview.  

Machine Learning Industry overview

As we said, the Machine Learning industry is up and running: 

  • According to Statista, in 2018, the entire ML business value has reached $16.9 billion, with 91 of machine learning deals globally.
  • Another Statista report shows us that machine learning apps attracted $29 billion during March 2019.

Machine learning industry

What benefits you can expect from implementing machine learning

  • After ML integration, 76% of businesses recorded higher sales. ML technology predicts better user behavior, optimize processes, lead up-sell, and cross-sell.
  • 50% of enterprises are applying machine learning to refine marketing issues.
  • ML has helped a few European banks to increase new product sales by 10%
  • Banks use ML to improve customer satisfaction

Now, let us dig deeper and find out what  Machine Learning is.

Machine Learning explained

Machine Learning (ML) is a technology of automated data processing and decision-making algorithms. Such algorithms are designed to improve their operation according to the results of their work. Basically, it means “learning on the go.”

The more qualified data ML has, the more accurate the ML algorithm becomes. 

To build a model that uncovers connections Machine Learning uses the following three algorithms: 

  • Supervised learning when an algorithm learns from example data and associated target responses. This data might include numeric values or string labels such as classes or tags. Later, when posed with new examples, ML can predict the correct response. 
  • Unsupervised learning. ML learns from examples without any associated response. Thus, the algorithm determines the data patterns on its own.
  • Reinforcement Learning. ML is trained to make specific decisions from the environment. In this way, the machine captures the best possible knowledge to make accurate decisions.

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Machine learning practical examples

As you can see from the chart below, machine learning is a very multidisciplinary technology used in the following industries: 

Finance

Machine learning for finance predicts future trends, bubbles, and crashes. For example, ML can analyze information about borrowers to determine their credit rating. Machine learning is also handy in process automation. What can machine learning be used for in finance? ML replaces manual work, increases productivity, and automates repetitive tasks. 

As a result, applications machine learning in finance brings the following benefits: 

  • Reduction of operational costs due to process automation.
  • More income, which is a result of enhanced user experience and better productivity
  • Improved reinforced security and compliance.  

Healthcare 

Another application area of machine learning is in medical diagnosis. In a healthcare system, machine learning combines the doctor's knowledge and makes the treatment more efficient and reliable. ML algorithm is used for diagnostic, personalized medicine, and other areas where time matters. The most notable healthcare machine learning usage cases are:  

  • Heart Disease Diagnosis
  • Cancer Detection and Prediction
  • Personalized Treatment

An example of machine learning applications in healthcare is our recent project. We developed and trained a Machine Learning Chabot for a patient support system. To find out more, read the case study. As well as this, we also took part in developing Nanopore DNA Sequencers, another machine learning project for healthcare with Machine Learning algorithm. Read the full case study in the link

 

Media and entertainment

In this industry, Machine Learning helps media companies to provide more relevant video content to on-demand platform users. In most cases, media companies use this technology for: 

  • Building recommendation algorithms
  • Creating custom artworks
  • Analyzing customer behavior 

Besides, ML helps TV channels to detect fact-manipulated propaganda and fake news. An example is AI VERSUS, the ML algorithm that we have been involved in. The project required building a conversation user interface for Russian TV channel  Rain. AI VERSUS shows two different points of view of the political situation in the Russian Federation. Check out more about this project in our case study

E-commerce 

Customer Support & Self Service Providing quality customer service in e-commerce is another machine learning application in business. Doing so at scale is daunting. But, one answer is to use machine learning technology like chat-bots. Intelligent chatbots use natural language processing to communicate with a customer, identify an issue, and resolve it. Automating customer support makes it easier for you and your customers to have higher satisfaction. There's a lot of creativity to how machine learning can be used to help customers, chat-bots being just one example. But the intent remains the same: higher customer satisfaction.

You may also like: Do You Need a Mobile eCommerce App for Your Business?

But how to use machine learning in e-commerce? Let us see.

  • Product Search
  • Product Recommendation and Promotions 
  • Trend forecasting and analytics
  • Fraud detection and prevention 

Here is a general workflow of "abnormal" behavior pattern detection:

Machine learning abnormal pattern

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Top Six Machine Learning Mobile Application Examples

Advanced algorithms transform the way users interact with their devices while bringing unique machine learning mobile app ideas. Our list of the best Machine Learning business applications includes the following examples: 

1. Snapchat. The application uses machine supervised learning algorithm for computer vision. Looksery, a Ukrainian startup, developed the algorithm behind the computer vision. Soon, the company was acquired with Snapchat for a whopping $150 million. Now, the mobile machine learning algorithm finds faces in photos to add fun elements like glasses, hats, dog ears, and more. For a more detailed explanation on how  ML Snapchat filters work, check out this article

2. Tinder uses an algorithm with reinforcement learning for the 'Smart Photos' feature, which increases the user's chances to find the perfect match. The app shows user photos to other users in random order. Then, Machine Learning analyzes how many right or left swipes each image receives. In this way, Tinder learns which photos are more attractive to other users. The algorithm reorders the user photos to put popular photos first.

To find out more about Tinder tech stack, read HOW TO DEVELOP A DATING APP LIKE TINDER IN 2019

3. Netflix. The app uses several reinforcement learning algorithms for personalized recommendations of movies and shows.  Besides this, Netflix uses machine learning for generating series and movie artworks. A computer vision algorithm analyses each movie screen to pick the best images and test them among the communities with a specific taste.

4. Yelp uses supervised Machine Learning to enhance user experience with “Recommended for You” collections. The ML algorithm skims the reviews for each restaurant listed. Then, ML determines the most popular dishes on how many times the meal was mentioned. Besides, Yelp uses ML to accumulate, classify, and label user-submitted photos with attributes. Such attributes include "ambiance is classy" or "good with kids" with 83% accuracy. 

5. Facebook applies a Supervised Machine Learning algorithm for detecting persons in images, news feed recommendations, friends suggestions, relevant ads and more. Also, Facebook uses ML to transform a panoramic photo into a 360-degree immersive experience. 

6. eBay applies a Reinforcement Machine Learning algorithm for the product recommender Chabot, called ShopBot. The Machine Learning algorithm helps ShopBot to understand what users are looking for. Chatbot then processes their text messages and images and finds the best match. eBay chat-bot has been praised for its excellent contextual understanding as well as its use of friendly language. 

As a result, ShopBot users are three times more likely to ask questions about specific products than those browsing eBay's inventory.

And now, let us examine the technologies to apply for your ML mobile application. 

Top Three Platforms for Developing a Mobile App with Machine Learning

To power your mobile app with Machine Learning you can choose one of the following solutions:

 

TensorFlow

OpenCV

MLKit

Description

TensorFlow is a machine learning framework from Google, used by Airbnb, DeepMind, and Google. With this solution, you can create and train a custom ML algorithm for a mobile app. 

OpenCV is an open-source library that includes 2,500 algorithms for machine learning and computer vision. You can use this library to make your mobile app recognize people's faces or printed text. Developed by Intel, OpenCV uses C++ as the primary coding language. Still, the library is also available for other languages, such as Python, Java, Ruby, and Matlab

ML Kit is a mobile SDK that brings Google's machine learning expertise to Android and iOS apps. MLKit allows implementing the required functionality in just a few lines of code. This SDK also provides convenient APIs that help you use your custom TensorFlow Lite models in your mobile apps.

Features

  • Pre-Built Algorithms
  • Computer Vision
  • Natural Language Processing
  • Artificial Neural Networks
  • Analyzing and processing images
  • Face recognition
  • Object identification
  • Gesture recognition in videos
  • Camera movement recognition
  • Building 3D models of objects
  • Searching similar images in a database
  • Text recognition
  • Face recognition
  • Barcode recognition
  • Landmark recognition
  • Image labeling
  • Object recognition

Pricing

Upon request

Free

Upon request

Top Seven machine learning tips for developers 

  1. The more data is provided to the algorithm, the more accurate predictions are. This means that the machine learning engineer should avoid subsampling and use all available information.
  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 predictions.
  3. Bring the data scientist to the project to choose the right method and parameters for the best results.
  4. Data is the basis of machine learning. Improper data collection can affect prediction capabilities.
  5. Understanding data features also have an impact on succeeding learning processes and predictability.
  6. While building machine learning algorithms, consider the business model and production capacities.
  7. ML algorithms require proper and careful testing which increases development costs and time.

Machine Learning Apps: Closing Thoughts

ML algorithms improve customer experience, maintain customer loyalty, increase engagement, and so on. This technology suits any mobile business app that needs predictions and has a large enough data set. Currently, there are many application areas of machine learning across many industries such as government, healthcare, transportation, and e-commerce. Depending on the ML  usage case, you can choose one of the Machine Learning app development platforms described in this article. 

Ready to empower your app with Machine Learning?

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Daria Dubrova

Content Marketing Manager