Why Should You Integrate Machine Learning into Your Mobile Apps?

Machine Learning Apps are fast invading our everyday lives as technology is progressing towards delivering smarter mobile-centric solutions.

Embedding mobile apps with Machine Learning, a promising segment of AI, is spelling out many advantages for the adopting companies to stand out amidst the clutter and rake in sizeable profits.

Many organizations are investing heavily in Machine Learning to reap its benefits. Based on a prediction, Machine Learning as a service market will touch $5,537 million by 2023 while growing at a CAGR of 39 per cent from 2017-2023.

What Are Machine Learning Apps?

Machine Learning Applications refer to a set of apps with Artificial Intelligence mechanisms designed to create a universal approach throughout the web to solve similar problems. The ML apps are based on a continuous learning process and provide end-users with an exceptional experience.

Machine Learning is implemented as software specifically developed to improve its behaviour and predictions by learning from the data it collects.

Basic Principle Behind ML

The functionality of ML is quite similar to the human central nervous system in nature.

To transmit every piece of information to the brain, the neural networks within one’s nervous system generate electrical impulses and quickly pass on the simplest signals, one neuron to another.

Machine Learning is based on implementing these fundamentals, and any project based on it goes through a very similar workflow.

Machine Learning Apps: Why Should You Integrate ML Into Your Mobile App?

Machine Learning empowers an app to recognize patterns and apply its learning to improve the user experience and eliminate friction continually. 

Most of the modern apps poised for success have embedded this Artificial Intelligence mechanism, working in the background.

 

Let’s look at some of the ways Machine Learning can benefit your mobile application:

How is Machine Learning Benefiting the Applications?

Machine Learning holds the potential to improve outcomes for mobile apps in the following ways –

1. Lend A Touch of Personalization into your App

In today’s world of limited product differentiation, personalization is one of the key ways to stand out amidst target groups.

Machine Learning facilitates the app functionality customizations for different users by analyzing the revealed data from their behaviour.

This helps in serving the most customized options and even the most relevant ads, optimizing marketing costs.

2. Provides an Efficient Searching Experience for Applications

With the data led world progressing at a high speed, efficient searching has gained even more importance in creating a good user experience.

Today, when users search for their queries on the internet, they expect the results to be closely aligned with their search intent.

Machine Learning apps make this extremely relevant, seamless and quick to achieve.

3. Provides Way To Detect And Control Fraud through Applications

fraud-prevention-using-ml

Machine Learning helps gauge if the app is vulnerable to security threats and plays a vital role in preventing fraud.

The analysis of GPS traces and usage patterns through ML tools assist a great deal in uncovering various suspicious activities.

This way, by analysis, continuous learning, and automation, Machine Learning helps applications adhere to and implement high-security standards.

4. Supports Applications with Visual and Auto Recognition

Using neural networks, applications with Machine Learning integration detect various faces and recognize different words to enable translation.

This is mainly helpful in making the experience seamless and less time-consuming for the end-users.

5. Helps Applications with Advanced Data Mining

The applicability of Big Data is uncontested and multifold. However, to process the massive amount of raw data, much effort is required to analyze and categorize the information.

Machine Learning is equipped to process multiple profiles at once and hence helps create well-aligned strategies for an app backed by robust data.

With all these good features, it’s time to adapt to their world-class applications across various industries.

Most Successful Industrial Applications Of Machine Learning

Owing to its myriad advantages, Machine Learning has been applied by various industries to augment the performance and usefulness of their apps; both for the end-users and the business itself.

Here are some successful applications of the same in different industries –

Ecommerce Machine Learning Apps

Machine Learning has benefited many e-commerce giants such as Amazon, Alibaba and eBay. Moreover, as such, e-commerce stores need to have a fat advertising budget for acquiring their customers.

The retention of their acquired customers and improving the lifetime value of each customer accepted is essential to boost their bottom line.

Machine Learning techniques have helped with this objective by simplifying cross-selling and upselling.

It enables an inbuilt mechanism to fetch the best product recommendations that align with a customer’s preferences, making cross-selling and upselling far more successful.

Moreover, Machine Learning has helped e-commerce operators in the following aspects:

  1. Shipping Cost Optimization
  2. Supply Prediction
  3. Demand Prediction
  4. Fraud Prevention

personalised-customer-experience

Machine Learning Enables Personalization Of Shopping Experience (Source)

On the other hand, customers using ML-based e-commerce apps enjoy a more personalized shopping experience with recommendations.

It consumes the least of their time and helping them make the best purchase based on their preferences.

Fitness & Health Machine Learning Apps

Machine Learning has been driving significant results in the fitness industry. It has enabled fitness providers to roll out personalized services for their users. 

For instance, ML empowers coaches to auto-curate a client-focused workout routine as per their fitness goals and body capabilities. It has helped to save a lot of time for users to improve on their form and technique.

Therefore, as a prominent example, the Optimize Fitness app is one that Machine Learning empowers. It helps its users gain access to a personalized workout routine with videos of the most relevant exercises.

Personalising fitness app using ML

Personalized Suggestions Given By Optimize Fitness App

In contrast, Machine Learning has helped reduce the cycle time of lengthy and expensive drug discovery processes. It has been deployed to determine genetic markers and genes to offer patients personalized treatment.

Machine Learning is also being used to aid in the speedy diagnosis of diseases and maintain intelligent electronic health records.

Taxi and Food Delivery Machine Learning Apps

The applicability of Machine Learning has also brought significant results in delivery services and cab aggregation.

The world’s most popular cab service, Uber, has created a great user experience for its active app users – riders and drivers- relying on Machine Learning.

Therefore, Uber app works with ML tools at the backend to help the riders with an accurate Estimated Time Of Arrival (ETA) and cost based on their trip details, factoring in the real-time traffic.

For the drivers, it presents optimized route information in real-time. Moreover, to arrest fraudulent transactions, Uber is relying on ML via practices like facial recognition or detecting the usage of stolen credit cards.

Uber has also integrated ML into its venture Uber Eats. However, It is providing its users with customized recommendations and precise ETA for their food deliveries.

Entertainment Machine Learning Apps

Machine Learning is also making the entertainment world more fun with endless personalisations. 

Among the entertainment applications, Netflix deserves mention. When it comes to watching online movies and TV Shows, Netflix is the most trending app for all age groups. Its rising popularity and viewership retention strategies depends a lot on its personalised shows and movies recommendations. 

Netflix has saved about one million dollars on account of the automated and personalised recommendations that its platform furnishes for users. 80 percent of the requests the app gives out can be attributed to its Machine Learning backend. 

Snapchat has also garnered significant popularity due to its filters that help app users spice up their photos with exciting options. However, with Machine Learning, the app has been made capable of performing face recognition, localising features, and adding filters very accurately.

face-recognition

The Snapchat App Face Detection Capability Rests On Machine Learning (Source)

Education Machine Learning Apps

Machine Learning apps enabled chatbots to answer students’ queries in real-time, assess their assignments and provide unbiased grading as per their performance.

It has witnessed a tremendous response in the education sector, and helped both students and teachers fair better in the knowledge exchange process.

Teachers as human beings will naturally have to go through a time limitation to analyze multiple students in detail and recommend better learning techniques or provide career guidance. However, ML-powered chatbots are seamlessly stepping in right here to help students.

Finance Machine Learning Apps

Machine Learning apps have also been helping financial organisations with significant optimisation. It’s turning out to be very helpful for banking and finance companies to scrutinise transactions of customers, social media activities and browsing history to arrive at accurate credit ratings. This done correctly has clear implications for profitability.

More importantly, Machine Learning is being deployed to prevent fraud by analysing transaction behaviour and offering personalised customer services.

Machine Learning has also been bringing in real benefits for personal finances.

For example, the Oval Money application works with ML techniques to help people budget and save optimally by analysing their spending habits along with that of the wider community.

This way, by automatically understanding behaviour and patterns on a large scale, the app is meting out a flexible and completely automated saving method to meet one’s financial goals.

Machine Learning principles of Oval Money App

Oval Money App Works On Machine Learning Principles (Source)

In short, the applications of ML have reached across various industry verticals to cultivate meaningful benefits.
Now comes the question, how to proceed with the implementation of ML in your app?
Is it that complicated? Well, with all the available open source libraries, it’s not.

How To Go Ahead With The Implementation of Machine Learning?

There are several open-source ML libraries available for app developers to embed in their applications, and many trusted companies are providing it to enable seamless adoption of this technology.

 

Five such well-known libraries are –

I. TensorFlow

It is an open-source ML library by Google Brain Team. It is helpful for numerical computation by using data flow graphs. It enables the execution of computations to numerous CPUS/GPUS in any device via a single API.

II. Core ML

It is specifically meant for iOS app developers; and it helps them integrate ML models into their applications. Core ML is well known to provide a boost to both app performance and efficiency.

III. Microsoft Cognitive Services

Microsoft Cognitive Services enable the developers to build smart apps. It empowers the apps to see, hear, comprehend and converse using natural means of communication.

IV. Amazon Machine Learning Services

Machine Learning Services from Amazon enables developers to build ML models using visualization tools and wizards. It also provides a mechanism to obtain predictions for mobile applications without the administration of any infrastructure.

For example, it can be used to forecast demand, predict frauds, and the number of clicks.

V. Ignio

It is an ML-based self-learning platform by TCS. It is designed to automate and optimize IT operations. It is pretty powerful in terms of grabbing environmental information and also resolving common errors by itself.

In case of inability to resolve, it passes on the problem to the human and trains itself for the future.

Bottom Line

To sum up, Machine Learning is among the most striking avenues of AI. It has massive potential in improving outcomes of mobile applications.

With its several advantages, it’s making applications user-friendly, full of utilities and also beneficial for the company that owns the app. It’s due to the same reason, that the most disruptive and popular apps have already adopted the technology and many others are fast adopting.

If you wish to build an app that creates real value for your user and your business; Machine Learning can be a safe bet to embed, so as to contribute towards your goal.

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