How Machine Learning Is Powering A New Generation Of App Development Posted on : Aug 03 - 2020

Ever since the introduction of computers, the primary objective of their evolution has been to take arduous calculations off our plates. It meant automating tasks that would otherwise take us a long time.

Over the past few years, the computing capabilities of mobile devices have reached a point where it's now easy to deploy machine learning natively.

Artificial intelligence is a term that gets thrown around a lot, but it's machine learning that's making automation possible. When we talk about artificial intelligence, we actually refer to its branch called machine learning, which is the way computers learn and perform tasks without being explicitly programmed.

Developments in machine learning algorithms have significantly helped and bolstered app development. Whether we talk about Android or iOS, the SDKs for these applications include several APIs that allow developers to tap into the machine learning capabilities of the device. The chips powering Apple's iPhones have a dedicated neural engine that can accelerate certain workloads. Similarly, Google's Pixel phones also incorporate on-device machine learning. These SDKs allow developers to harness hardware prowess for their apps.

Such development in machine learning could not have come at a better time; it converges perfectly with the proliferation of big data. More devices are connecting online, and more users are signing up for services, and the explosion of the IoT ecosystems means that the need for expediting existing processes is the need of the hour.

In the mobile app development space, services can find patterns in the big data collected from their users. Machine learning algorithms can make use of unstructured data and provide useful insight into user behavior thanks to this data. This paradigm shift means that more clients are asking for tools from software developers that leverage ML to improve services, such as learning about what users are interacting with and what has proved to be a sore point for them.

User experience is one of many keys to success, and modern reporting tools powered by machine learning deliver valuable insight into this area of interest.

Facebook, for example, uses its ML tools to give you a personalized experience. It is also using its tools to predict user behavior. This enables the social media giant to target relevant audiences for advertisements. If you are likely to do something in the future, the advertiser will mark you as a potential customer or try to retain you if you are on the verge of shifting to a competitor.

A ride-hailing or food delivery service can use data from previous rides and apply machine learning to more accurately estimate the time of arrival and cost of the trip based on factors such as traffic, time of day and weather conditions. Camera apps can use algorithms to reduce noise and correct HDR and exposure by taking multiple shots, analyzing them, then creating a cleaner, better-looking resultant image. View More