Is ML Powering Mobile Applications ?

For the users who are not really connected with the Software Development or those people who are not aware about the technical terms and technical world are really huge group.They have WOW! 😱reactions while using mobile applications like Facebook when app automatically tags their friends on photos or Spotify suggests them their favourite song or I can say songs of their tastes or Myntra 🛍 Suggest them better dress they are looking for. All these things are actually making Mobile applications powered and attracting users to use mobile applications more often to make their life easy.

What is Machine Learning(ML) ? :

ML is a platform which is making automation possible that means ML is a way through which computers learn and perform tasks without being explicitly programmed.So basically ML use historical data as input to predict new output values and provides output based on history data we have provided but as ML takes millions of data as input to get trained , output from it , is powerful and provides nearly accurate output.

Benefits of Machine Learning in Mobile :

Lower Latency :

Mobile app developers know that high latency can be the death knell for an app, regardless of how strong the features are or how reputable the brand is. Android devices have had latency issues with a number of video apps in the past, resulting in viewing experiences with out-of-sync audio and video. Similarly, a social media app with high latency can make for an extremely frustrating UX.Performing machine learning on-device is becoming more important precisely because of these latency issues. Consider social media image filters and location-based dining recommendations — these are app features that require low latency to deliver results at their highest level.So for the lower latency , Smartphone manufacturers and the big tech players are catching up with this realisation. Apple has been leading on this front, developing more advanced smartphone chips using its Bionic system.So it will make mobile apps even more smooth and smarter with ML.
Increased Security and Privacy

When it comes to mobile applications , now a days smart users put security of their data and privacy of them selves first.And ML can provide huge benefit on this by on device machine learning.Because data doesn’t need to be sent to a server or the cloud for processing, cybercriminals have fewer opportunities to exploit any vulnerabilities in this data transference, thus preserving the sanctity of the data.The best example for this is iPhone’s Face recognition feature.It serve as the backbone of Face ID. This iPhone feature relies on an on-device neural net that gathers data on all the different ways its user’s face may look, serving as a more accurate and secure identification method.
No Internet Connection Required

Beyond issues with latency, sending data to the cloud to be processed for inference requires an active Internet connection. Oftentimes, this works just fine in more developed parts of the world. But what about in areas of low connectivity? With on-device machine learning, neural networks live on the phones themselves.This allows developers to deploy the technology on any device at any given time, regardless of connectivity.HealthCare is one industry that can benefit greatly from on-device machine learning, as app developers are capable of creating medical tools that check vitals, or even ones that allow for remote robotic surgery, without any Internet connection. The technology could also help students who may need to access classroom material in a place without connection, such as a public transportation tunnel.
Reduced Costs for Your Business

On-device machine learning is also designed to save you a fortune, as you won’t have to pay external providers to implement or maintain these solutions. As previously mentioned, you won’t need the cloud or the Internet for such solutions.Avoiding the heavy, data-processing nightmare between mobile and the cloud is a huge cost-saver for businesses that choose on-device machine learning solutions. Having this on-device inference also lowers bandwidth demands, ultimately saving a hefty sum in costs.

Use cases Of Machine Learning which helps you to blow an idea with your requirement :

AI-powered financial assistant

You can use such mobile applications, powered to receive insights into your personal finances.In most cases, such apps are developed by banks to provide clients with additional value.By leveraging machine learning algorithms, the app analyses your transaction history and comes up with expenditure predictions, track spending habits, and gives financial advice.For an example Bank of America is having a mobile Voice Assistant which provides more personal assistant in terms of finance and convenient banking to 25 million mobile app customers.
Fitness mobile apps with ML

Such workout apps, powered with machine learning capabilities, analyse data gathered from wearables, smartwatches, and fitness trackers. Then, depending on one's goal, the user receives personalised lifestyle advice.The algorithm also analyses the user's current fitness levels and eating habits to provide customised fitness plans. Aaptiv Coach is one of the most prominent fitness apps with machine learning.The app gives users an order of workout, including custom workouts from Aaptiv, and measures the user's progress.
Healthcare mobile applications with ML

Numerous condition-based mobile applications help users to keep track of heart illnesses, diabetes, epilepsy, and migraines. Thanks to machine learning algorithms, such apps analyze user input, predict the possibility of one or other conditions, and notify the doctor about the patient's current condition for streamlined treatment.
E-commerce

Online retail mobile apps can use machine learning algorithms in several ways. For example, such algorithms are handy to provide the buyer with more relevant product recommendations based on purchase history, identify fraud with credit cards, and visual search.

And Many more applications are possible. Even ML/AI took place in almost every use case. If you are running a business and you also have such smarter users then you can choose AI/ML with your applications and make your users even smarter with your smart app by providing them their taste without asking a lot of questions and irritating them.