Machine learning and mobile development: how far can we get with smart gadgets?
There are about 4.7 billion mobile users in the world — can you imagine how many mobile apps each one of them uses and how many apps are there in general?
Mobile application development is trending. Developers are constantly coming up with new ways to delight the users and bring in some brand-new experience. Users, in turn, raise up their expectations and strive for something unique or at least corresponding to modern trends.
Machine learning has been penetrating the industries for a few years already. Now we deal with this technology on a daily basis: when opening a Snapchat app or tagging people on Facebook.
But what is the potential of ML in mobile development and how is it used now? Let’s see the development pattern.
Machine learning 101: basics that you need to know
Machine learning enables software (machines) to “think” independently and make decisions or predictions without being explicitly programmed to do so.
This technology is a subset of artificial intelligence and has been serving various industries for a few years already. The biggest advantage of ML is its ability to work with an enormous volume of data, process, analyze it, and then make accurate forecasts based on the data.
Financial and healthcare industries already use machine learning in their processes. In finance, machine learning helps make financial forecasts and serves in the lending industry to estimate the credibility of the borrowers. In healthcare, machine learning helps predict diseases and serves as a base for creating personal virtual assistants for the patients.
Other industries benefit from ML too, but the one that interests us the most is mobile development. It already uses augmented and virtual reality and builds apps on the base of AI. So what about the ML use in the development of mobile applications?
ML for mobile: what we have for now
The use of machine learning in mobile app development is already vast and covers some of the most essential needs of the users.
Because ML is able to collect, process, and analyze the data, you can use it to learn about the customers’ preferences, hobbies, behavioral patterns, etc.
This opens up a great field of opportunity for personalization, which is the number one priority this year and the upcoming ones. With 48% of the shoppers willing to wait a bit longer just to get a personalized product, think about how important personalization really is.
Here are a few most commonly used examples of personalization in mobile apps:
- F&B: smart bots in the business apps of restaurants and fast-food chains provide users with personalized offers;
- Transportation: Uber-like apps are able to estimate the time of arrival and the remaining time until the destination due to ML technology;
- Healthcare: personal virtual assistants learn the information about the patient and are able to predict the time of a certain disease (like a headache) and provide personalized recommendations;
- Fitness: some fitness apps analyze the generated data (like weight, heartbeat, overall amount of physical exercise) and provide a unique program.
Machine learning allows almost any app to add personalization to its experience and leverage customer satisfaction from using the app.
Users search for information on a daily basis and ML can make the search process faster and more interactive.
Voice search and image search are expected to become the common forms of search in the near future. This will be especially relevant for e-commerce.
Pinterest, for example, already introduced a few features: Pinterest Lens and Shop the Look. They are based on object scanning and aim to make the shopping easier and faster. The Machine Learning technology allows users to shop for the items that are similar to the ones they scan and also find relevant things that would go well with this particular item.
As for the voice shopping, it’s not so big yet but is expected to hit $40 billion in 2020. One of the main representatives is Alexa by Amazon that can put items in the cart, receive and process the order, and inform on the order status.
In addition, a smart search is valuable for marketers and business owners. Because they can analyze the data and users’ shopping and search behavior, they will be able to tailor the search results to rank higher and become more relevant.
ML is all about data and data is an incredibly powerful asset, if used right.
With the help of Machine Learning, business owners can analyze the age, gender, search frequency, requests, and other information about their users — and deploy it to build a forecast about the future user behavior.
By knowing how your customer may most possibly act, one can build a solid and relevant marketing strategy and increase sales and user engagement significantly.
Machine learning provides better security by allowing a biometric recognition type and this can be especially important for the e-commerce mobile apps as they process money transitions and contain customer data.
In addition to enhanced security, biometric recognition is also easier and faster than typing in lengthy passwords. And this is exactly what users crave for today.
Another aspect of enhanced security is fraud prevention with ML. The technology can automatically monitor the processes that take place within the app and immediately inform you in case any suspicious activity occurs.
We already mentioned this above but the topic is too important and big to just leave it out in a few sentences.
Machine learning allows creating bots that grow and become smarter as they learn more about their user. Such bots are already used in:
- Finance: can help perform minor operations like money transfer or advice on certain questions;
- Healthcare: help monitor the health and notify the doctor in case of an emergency;
- F&B: provide personalized offers of restaurants or places to visit;
With the help of personal assistants, the user gets access to information faster and easier. This, in turn, leads to better customer experience and contributes to rising sales.
Things to consider for mobile app developers
Now that you know about how ML can transform one’s app, it’s time to learn what tools you may need and essential things to keep in mind when developing an ML-powered mobile application.
There is quite a number of frameworks available for ML-based development. Here are some of the most widely used:
- Google Cloud ML/Google Vision: Google designed this framework specifically for “image analysis”. Some of the features included are text and face detection and content moderation.
- PyTorch by Facebook: this library is based on Python and is most commonly used for apps like language processing. The library is open-source and is loved by deep learning researchers.
- TensorFlow/Keras model: this is another framework by Google. It is an open-source one and general purpose one and is mostly used for developing and deploying the ML models.
- AWS suite: probably, the most comprehensive of them all, the AWS suite offers a wide range of features. They include image analysis, chatbot, text to speech, and many others.
These are not the only available ML frameworks to use in your work. Do some research and find the one that will 100% correspond to the needs of your app.
Tips to remember
Machine learning is driven by data and this is the most important thing to keep in mind. Below you will find some useful tips that may come in handy when you get down to the app development:
- Provide as much data to the algorithm as possible. The more data is there, the more accurate the outcome will be.
- Make sure the data is suitable for processing: i.e. it does not have “raw” data in it or data in a format that cannot be recognized by the algorithm.
- Consult with the data scientist to choose the best ML method and parameters.
- Invest in testing before launching the app to ensure accurate results.
Machine learning is a complex technology which requires a knowledgeable approach. But if you manage to get it right, the results will overcome your expectations and will instantly leverage the performance of your app and your business growth.