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How Are Developers Using Machine Learning for Mobile Apps?

Read Time: 3 minutes

The power of machine learning to benefit mobile apps is remarkable. Although machine learning technology was initially conceived by tech pioneer Arthur Samuel while working at IBM in 1959, it has only recently entered the mainstream. An increasing number of app developers are opting to use machine learning in conjunction with other cutting-edge technologies such as artificial intelligence and predictive analytics.

Machine learning gives your app the ability to learn, adapt and improve over timean impressive feat considering that just a few years ago, an explicit directive from a programmer was the only way to prompt a computer or mobile device to execute a particular action. This meant that developers had to foresee and account for every conceivable need or scenarioa monumental and impractical task. And that’s precisely where machine learning for mobile apps enters the equation.

How Does Machine Learning Work?

Machine learning refers to technology that allows a computer (or another device with computing powers, such as a mobile device) to process data, identify trends or patterns and then take actions to help fulfill a specific objective.

This technology is rapidly becoming a favorite amongst developers and companies alike, as machine learning allows for greater efficacy without a corresponding increase in programming/development costs and timeframe.

How Are Developers Using Machine Learning for Mobile Apps?

Exploring the realm of machine learning for mobile apps is interesting because there are a plethora of potential uses for this technology. Here is a look at a few approaches that mobile developers are currently utilizing:

  • Machine Learning for Artificial Intelligence: Machine learning is a vital component of artificial intelligence technology. In fact, you might think of machine learning as the “brain” of a device with artificial intelligence capabilities. For instance, the machine learning interface would analyze data streams, then algorithm updates would be implemented in order to achieve an overarching objective. This technology allows artificial intelligence to adapt and respond to new scenarios that developers did not account for.
  • Machine Learning for Predictive Analytics: Machine learning is quite powerful when paired with a predictive analytics engine. Predictive analytics engines process large volumes of data, which are then used to make predictions or recommendations. Shopping recommendations on ecommerce apps are a wonderful example of this technology at work. But alone, PA technology is not capable of handling the unexpected. It’s simply not feasible or practical for developers to program the PA engine in a manner that accounts for all possible conditions and predictions. By adding machine learning, the predictive analytics interface becomes adaptable and flexible in a manner that can dramatically improve its overall efficacy, accuracy and utility.
  • Machine Learning for Filtering and Security: Machine learning technology is extremely effective for applications that require some form of filtering or protection in response to ever-changing input. For example, machine learning can identify suspicious activity within an app, even though the nature of that activity may be constantly evolving. Similarly, machine learning is effective when applied to email and forum filtering functions. The spammer’s IP address and email address is always in flux, and without machine learning capabilities, a person would need to manually identify spammers and then add an email or IP address to a blacklist for filtering or blocking. But machine learning automates this process, with spammers being identified and blocked without a developer’s explicit programming instructions.

These three methods are just a few of the many potential uses of machine learning for mobile apps. Even something as seemingly simple as optical character recognition (OCR) can benefit from machine learning capabilities since it’s conceivable that a developer may omit some of the countless possible variations in character shape from the original algorithm. Machine learning would give an OCR app the power to identify (and “remember”) characters that are written in a new manner. This app could also theoretically identify new characters or symbols that developers had not considered. The same concept is true for natural language processing (NLP) apps. This is good news for companies that want an app developed, as machine learning will reduce the amount of time spent updating and fine-tuning a number of different algorithm elements.

Integrating Machine Learning Capabilities in Your Mobile App

Virtually all cutting-edge technologies can benefit from machine learning. This includes everything from predictive analytics and natural language processing to augmented reality, virtual reality and artificial intelligence. At 7T, our developers specialize in these state-of-the-art technologies and many others, making us well-positioned to develop a mobile app that’s adaptive and efficient (both in terms of performance and maintenance requirements).

7T works with clients across Texas, from Dallas to Austin, Houston and beyond into other areas of the United States. We encourage you to reach out to our team today to discuss your next app development project.

Reach out to our team today!

Venkatesh Kalluru

Venkatesh “VK” Kalluru is a technology and business expert with executive and hands-on experience in automating multi-million dollar enterprises and a strong record of success in creating robust information technology architectures and infrastructures. VK brings proven ability in using IT to solve business issues to the Sertics team. Sertics is SevenTablets' sister company, specializing in predictive analytics solutions.

VK studied computer science at Jawaharlal Nehru Technological University in Hyderabad, India and earned a Master’s Degree in computer science at George Mason University.


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