Snapchat filters that make you look picture perfect.
Tinder adds love to your life.
Tesla car driving itself.
Financial prediction apps that give you expected returns on investment.
All these seem like magic, isn’t it? Well, that magic is called machine learning.
Before we talk further about how you can make the most of machine learning and include it in your mobile app, here’s a quick refresher about what machine learning is.
Machine Learning – What, Why, And How
When a software application is programmed to learn and improve on its own, using the received data, it is called Machine Learning.
You don’t explicitly program or tell the software app to do something or act in a certain way. It does it on its own accord by learning how to react to a stimulus based on historical data.
This helps offer a high level of personalization in mobile applications.
Did you know 57% of customers were willing to share their data with companies that offered personalized offers.
Sounds cool, isn’t it? Machines and apps working, learning, and improving on their own. Giving end-users access to a truly customized app experience. And businesses gaining customers, their appreciation, and ultimately more business.
And yet, not many businesses have developed machine learning mobile applications.
Which brings us to…
What’s Stopping Everyone From Using ML In Mobile Applications?
There are not one but many challenges facing businesses that hinder the development of machine learning mobile apps.
The most pressing challenges include –
Mobile Resource Usage
You don’t need us to tell you how annoying and frustrating are the apps that drain the mobile battery and use too many resources. We hate such apps, you hate such apps, and so do your end-users.
While using machine learning mobile applications, battery and device resource usage are top (yet, often overlooked) concerns.
You cannot do everything you did on the cloud on a mobile device due to resource constraints. Also, provisions need to be made for all types of devices. (You know the list of device configurations and types is ever-growing, right?)
This adds more parameters to the monitoring list.
Finding The Right Training Data
Generally speaking, there is no shortage of data. You can get as many numbers as you need. But, when you need specific data from mobile devices, that’s when you realize that finding the right data is like looking for a needle in a haystack.
Even if you accomplish this feat, you’ll need to maintain at least two separate databases. One for iOS and Android each. And more if you’d be extending your app’s availability to other devices.
Also, ever thought about what would happen if the same user uses your app on multiple devices? Well, you will need to find a way to make sure that the data remains accessible to the ML system across devices. This mechanism for sharing the model across devices isn’t a simple job.
On Device ML Training
Machine learning mobile app systems are still relatively new. This means there is not much information, guidance, knowledge, or experience available for training the machine learning system on mobile devices. What usually happens is developers train the system on servers and use it on mobile devices later.
The training happens on the server, and ML in mobile apps is mainly used just for inference. This can cause discrepancies. It is especially so when the machine learning system is expected to learn from each user and not a generic dataset.
For example, if you are developing a predictive keyboard that has been trained on offline public data, its predictions would still be generic and not customized as per every user of the keyboard. If ML training happens on the device, it will learn from the actual user over time and offer much better predictions about what the user is going to type next.
APIs Are Reduced To Being Just Bells and Whistles
A lot of AI and ML APIs (Application Programming Interfaces) are available today. And instead of reinventing the wheel, app developers can just use them in their apps to add specific functionalities.
However, ML is a continuous learning process. More data would mean better results. And existing APIs seldom meet the specific data requirements for specific niches.
Calling an API just for the sake of adding a cool feature is futile. It would need refinement. Also, using third-party API means you will have to adhere to their T&Cs. You also stand the risk of accidentally passing on confidential data from your app to a public API.
Unless APIs are custom made, they are mere additions of bells and whistles that don’t serve a purpose and just clutter your app, bringing down the overall user experience.
Reasons Why YOU Need To Find Workarounds For These Challenges
A breakthrough in machine learning would be worth ten Microsofts. – Bill Gates
That statement should inspire enough will to overcome all possible challenges you might face while developing machine learning mobile applications.
Do you still need more reasons? Here they are. The importance of machine learning mobile applications and why should developing an ML mobile app now –
You need to understand who your customer is, what are his likes/dislikes, what’s his intent, what he wants, and what he can afford.
This is important to personalize the app experience. Machine learning makes it possible as it is continuously analyzing more data and becoming better at what it does.
Your users will actually feel your app is talking to them.
Search functionalities are more of a norm today than a novelty. Machine learning makes the search results faster and more relevant. For one, your database is systematically arranged. And then, data about user intent and expectation is being collected. This makes search results in line with the user’s expectations.
Understand The User And Predict Their Behavior
If your app is used by women over 30 years of age, it is logical to direct your campaigns and ad efforts towards the right audience group, isn’t it?
ML collects user data like age, gender, location, interests, etc. This data can then be used to predict user behavior and adopt a proactive approach rather than just being reactive.
Users hate ads. But not when the ads are relevant. Relevant ads at the right place, right time not just get leads but also conversions. And ML can help you do that.
With such apps, you can understand users and their intent and even predict actions. Then place ads when and where buying intent is the highest. A cakewalk to conversions.
Machine learning mobile apps are more than just funky features. With ML, apps can have secure and streamlined login options too. Fingerprint, voice, and face lock, for example.
ML-powered mobile applications can also help users easily log-in to other apps and websites with quick, no-hassle authentications.
Deep User Engagement
Whether it be the cool features, efficient customer support, or elements of entertainment, ML ultimately enhances user engagement.
Real-time speech translation, smart chatbots, plenty of filters, and intelligent request handling are a few wonders of machine learning that deepen and enhance user engagement.
ML in Mobile Application In Action – Stunning Examples That’ll Blow Your Mind
You know Snapchat, Netflix, Tinder, and even Google Maps utilize machine learning in their mobile applications.
But is ML just for the bigshots? The following examples prove otherwise.
Check out a few stunning examples of ML being used in mobile applications that will blow your mind. Get ready to go Wow!
- Taco Bell (the American fast-food chain) uses TacoBot to take orders and give menu recommendations based on user preferences.
- Zomato, Uber Eats, Swiggy, and other food delivery apps predict estimated delivery times based on real-time traffic analysis.
- Uber, Ola, and other ride-booking apps show ETA and estimated ride fares in real-time.
- ImprompDo lets users manage time by prioritizing tasks and informing them what can be done at what time using ML.
- Migraine Buddy is a patient aid app that predicts possible headaches depending on collected user data. It also suggests ways to prevent headaches.
- JJ Food Services’ app employs ML to builds preference profiles and anticipates customer orders.
- Prisma, an image editing apps, lets users use a sizeable collection of filters by just telling the bot what they need.
- Penny, a financial management application, links users Venmo and PayPal accounts and credit cards to give smart suggestions.
Do This Before You Leap Towards Machine Learning Mobile Application Development
You now know that machine learning mobile applications are not a new phenomenon. A lot of ML mobile apps are already gracing app stores. You know it is important, and you know it is challenging nonetheless.
The next step from here should be to look for experts to steer your ship. Don’t try to test waters alone. You’d drown, and you’ll take your business along. Instead, hire app development experts who know what it takes to stay afloat. Let the hunt begin.