How Machine Learning Is Powering A New Generation Of App Development

How Machine Learning Is Powering A New Generation Of App Development

How Machine Learning Is Powering A New Generation Of App Development

Machine Learning – 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.

ML can also be used to identify objects. In an example of a shopping app, a user can simply point to an item, and the app will find matching results online.

Continuous training of models works in favor of security as well. Models can be trained to become faster and more accurate over time in facial recognition and biometric verification. Similarly, speech recognition can be trained, set up and improved as the user continues to use it.

Service-based apps aside, video games also benefit from machine learning. The term artificial intelligence is closely related to video games, as many game designers aim to depict the behavior of their characters as realistically as possible.

As you might have already imagined, the application of machine learning seems endless. The results achieved at the far end of the output tunnel will only be as good as the algorithm used, which is why deliberation is required during the software development stage to select the right algorithms.

Artificial Intelligence Simplifies Processes

AI and cloud computing pair together exceptionally well. While ML can be done on a device, latency insensitive things can be offloaded to the cloud; this is where the hybrid cloud approach comes in.

Companies today, especially startups, don’t have to invest in servers to establish their services. That aspect of business can be addressed by deploying third-party cloud services that offer significant power at the developer’s disposal for ML.

But it is not as easy as throwing money at a problem and expecting things to be running in a week. Machine learning requires a lot of trial and error to get to the point where you want the desired output. It begins with a team of data scientists who have the expertise to handle data. The best approach is to have a pilot program internally first. If you don’t have the capability or time to set up models yourself, many open-source models are at your disposal for training.

Technical expertise, support, pricing and data security are the vital factors you need to look at when deciding on a provider. Storing data on the cloud means it is critical to ensure that the cloud service provider has a proven track record of preventing security breaches and downtime.

The Future Of App Development

There is always the option to build your own servers for deploying and training a neural network. But what you save in cloud storage cost may end up being spent on keeping your infrastructure operational. Not only do you need to factor in scalability, but you also need to ensure the configurations of your server meet your desired performance output. Also be sure to identify what you’ll need to invest in its constant supervision — and what you might have to invest if anything goes wrong. One of the reasons why even multi billion-dollar companies use cloud services is because of their ease of distribution.

These are some of the decisions you will make while building your service. App development integrates many challenges, and these business decisions will trickle down to your user experience. Be sure you’re doing what’s best for your end user.

This article originally appeared on To read the full article and see the images, click here.

Nastel Technologies uses machine learning to detect anomalies, behavior and sentiment, accelerate decisions, satisfy customers, innovate continuously.  To answer business-centric questions and provide actionable guidance for decision-makers, Nastel’s AutoPilot® for Analytics fuses:

  • zAdvanced predictive anomaly detection, Bayesian Classification and other machine learning algorithms
  • Raw information handling and analytics speed
  • End-to-end business transaction tracking that spans technologies, tiers, and organizations
  • Intuitive, easy-to-use data visualizations and dashboards