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The Next Phase Of Machine Learning In Mobile App Development

Machine Learning

Artificial Intelligence and Machine Learning are the hottest topics frequently discussed as an essential marketing tool for the next-gen mobile apps. These technologies have brought massive changes to the ways we interact with mobile devices today.

Most startups and tech giants are going up to eleven to use machine learning in mobile app development. Machine learning (ML) is indeed outperforming a number of existing approaches that were much more complex and domain specific. And then, as witnessed, it is giving the first-mover advantage to mobile app services who’ve implemented the concept of ML.

  • Today, 39% of businesses use AI and ML technologies in some form.
  • A survey by McKinsey and Co. revealed that total investments in machine learning and AI developments tripled between 2013 and 2016.
  • Gartner predicts that by 2020, AI technologies will be ubiquitous in new business software and will be among top five investment priorities for 30% of CIOs.
  • According to Allied Market Research, machine learning as a service market is expected to reach $5,537 million by 2023, compared to $571 million in 2016.

How To Implement Machine Learning

This is the question we have been asked by many developers, business groups and enterprises. Honestly, the right implementation of ML has never been easier to pick up. Developers think that only top-level Data Scientists have the acumen to execute machine learning. But this is a mere misconception. Really!

In developer’s language, if you can make a REST call, you can use machine learning in your mobile apps. Also, if you can’t develop your own ML software, what you can do is use the fully trained machine learning APIs built by several companies like Mobile Vision API by Google Play Services, Google Cloud Machine Learning APIs. These services are pre-trained and ready-to-use for tons of interesting intelligence tasks.

Mobile Vision API allows developers to use the Android device camera to detect faces, recognise text and scan barcodes. So, any Android business app can make use of these services in their app. This Vision API by Google can be useful for social apps, translation apps, inventory management apps. Similarly, the iOS realm also offers such ML APIs to make your apps more intelligent and assist users like a human.

Besides, you can either train your own team to work on machine learning tactics or hire an app development company that can offer you the right resources to add the machine learning capabilities to your existing app model or develop a new ML-equipped app for you.

The Application Of Machine Learning

Being a multidisciplinary field, machine learning can be applied to any of your apps to enrich user experience and surge your business. The various industries where you can use this technology are:

E-Commerce Realisation

  • To provide relevant information to users while they search products.
  • In recommending them the right products as per their interests, past shopping experiences, and sending out relevant promotional offers.
  • Aggregating fashion trends and sales information from different sources and giving predictions in real-time.

Finance Management

Machine learning can be used for predicting future trends and crashes in the finance industry. By tracking the history of the previous transactions of a customer, a finance app can suggest them the right schemes for savings and investments. Without involving a human, this can be achieved through an algorithm programmed in a machine much faster and accurately.

Social Media & Entertainment

Just like Snapchat uses it for face detection and Facebook uses the bot technology, you can use ML capabilities in customising as well as advancing your social media app.

Enterprise Apps

By incorporating intelligent algorithms, you can automate the process of managing the hundreds of thousands of customer-service emails that your company receives.


Google has recently developed a deep machine learning algorithm to identify cancerous tumors in the human body through highest-quality biopsy results. Health apps can use pre-trained APIs to analyze data and health history of the patient and loop it back to physicians in real-time for providing the right clinical aid.

Some Of The Top Machine Learning Applications

We have various real-life examples to tell you that how some of your favourite apps have taken ML seriously and are delivering the best experiences to their users/customers.

  • Yelp – image curation at scale: Yelp’s machine learning algorithms have helped the company’s staff to compile, label and categorise images more effectively when they deal with millions of photos each day.
  • Snapchat Filters – face recognition: It isn’t easy for computers to recognise a face. To develop an incredibly brilliant algorithm, Snapchat puts it facial tracking algorithm to test by looking at thousands of faces so that it learns what a face looks like. And it’s doing amazing in the field.
  • LeafSnap – identification of the species: Just like the face identification in Snapchat, LeafSnap is an app for botanical students and experts where they can use the app to identify the species of any plant out there. With its machine learning algorithm, the app has the intelligence to identify the species of a tree/plant from a photo of its leaf. Determining the spices of the fossils like leaves is tuff though.
  • SwiftKey Neural – suggestive typing through neural networks: Though there are many chat apps that make typing on mobile apps easier by taking a guess of what the next word will be, they aren’t as good as SwiftKey Neural. The app takes it to the next level by getting a much deeper understanding of the context of a conversation with a machine learning technique called neural networks.

So, machine learning can tailor your app according to the personal needs of each user. This establishes a more stable and strong connection with the users.

Expert Views On The Future Of Machine Learning

Many international market reports and studies suggest that machine learning has outdone multiple humans and the game. One of the AI pioneers, Chris Boos in his recent interview said, “People are not made to work like a machine that most organisations put them through. They are meant to think of better, creative ideas than doing the age-old machine jobs.” He further added, “We have had industrial revolutions before but with machine learning and artificial intelligence, we have been able to replace the classical administrators with machines so that people can do much more than and move to the next-level in technology.”

Another breakthrough statement that we recently read is of CVP of Data Group and Machine Learning at Microsoft who mentioned, “Everything at scale in this world is going to be managed by algorithms and data. Every business in the near future will be an algorithm business.”

This means that machine learning algorithms can greatly augment human capabilities. Every company is now capable of accessing algorithm intelligence, and every app can now be an intelligent app. Thus, mobile app development is nearing to bring a more automated experience to the users.  You need to buck up for that. If your mobile app isn’t creating the appropriate algorithms to generate the highest-quality data that can be profitably used to engage more customers and bring in more revenues, your business is at stake. Think about it!

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Vipin Jain is the CEO and co-founder of Konstant Infosolutions, a mobile app development company offering feature-rich native and hybrid apps. With his business acumen, Vipin has honed the company’s vision, corporate structure and goals, and made it stand out among global competitors.