What To Know About Machine Learning For Mobile Apps

Machine learning is everywhere. If you pay attention you can observe it immediately, especially in the daily tasks completed with your mobile device. But in order to notice if you need to have some basic information about machine learning. In simple terms, we can define it as software created with the purpose to improve its behavior based on previous activities. Now, let’s see what else we can learn about this technique.

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What Machine Learning Brings To Your App

Taking into account that machine learning is suitable for predictive actions we will take a closer look at what makes this technology a good fit for mobile apps.

Image Source: https://theappsolutions.com/blog/development/machine-learning-in-mobile-app/



It is very hard to match your app’s functionalities with different groups of users. Think about transportation apps when you deal with both clients and drivers or kids apps when you need to convince parents and children about the benefits provided by your app. The answer is to analyze the data with the help of machine learning and to offer everybody what they really want.

Efficient Searching

When users enter specific keywords in the search fields, they expect to receive the write data according to their concerns. It is imperative to prove them that you can solve their problems like you promised. Otherwise, they won’t open your app again.

Fraud Control

We need to mention machine learning’s usability in mobile marketing when you must serve relevant ads to your target users. Moreover, this technique helps you to understand if your app is vulnerable or it is trustful enough to provide data following high standards of security.

Visual And Audio Recognition

Due to neural networks which is a special model of machine learning technique, apps are able to detect various faces with the purpose of adding different masks and to recognize different words for translate features.

Advanced Data Mining

Big data is a great source of solutions for all domains but it requires a lot of effort to analyze and to categorize the amount of information gathered. Machine learning has the power to observe multiple profiles when you want to create targeting strategies for your app.

How To Integrate Machine Learning To Your Mobile App?

Now that we understood how machine learning improves users’ experience it is time to present you a few tools which will help you to achieve that.


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Tensorflow is an open source machine learning library from Google. You can use it for mobile apps taking advantage of the many interesting elements offered by its implementation. It uses data flow graphs for numerical computation. It is able to recognize certain items but remember what we told you before, that apps powered by machine learning processes need to learn from millions of samples. For those interested in discovering more about Tensorflow you will find a lot of tutorials and useful documents on the official page.

Amazon Machine Learning

Image Source: https://aws.amazon.com/machine-learning/details/

Amazon ML is offered … well, by Amazon. With this tool, it promises to provide easy to manage solutions for creating machine learning models even for beginners. The best part is that you have the possibility to scale as your business grows but it is better to consult the pricing list before starting with the development process as you pay just for what you use. A very interesting list of the key features is available for app owners who are looking to implement smart APIs and real-time predictions for their creations.


Image Source: https://wit.ai/community

Wit.ai offers the proper environment for building all types of solutions including mobile apps using natural language. You will be amazed to see how fast you can transform user input into different actions. Wit.ai says that you will be able to accomplish that in less than 5 minutes but you will have to convince yourself. In case you want to discover more, feel free to check the documentation.

Core ML

Image Source: https://developer.apple.com/machine-learning/

It is impossible to create a section with tools provided for implementing machine learning when you want to create mobile apps and not to mention the framework from Apple which is Core ML. This service includes amazing features like face detection and face tracking, text detection, image registration, barcode detection and many more. Read more on developers’ page and select the right model for your app.

Examples Of Machine Learning

If you are impressed by all the above, some examples will enhance the way you understand this technology.

Snapchat Filters

We talked about Snapchat several times before and we also mentioned about the face recognition feature adapted after Snap acquired Looksery, a Ukrainian startup. Apparently so simple, the algorithms behind Snapchat Lenses are very complex and they are based on machine learning. Maybe the fact that it is used for transforming images into goofy characters provides this feeling.

Image Source: https://petapixel.com/2016/06/30/snapchats-powerful-facial-recognition-technology-works/



We all know Shazam app, but how often did you wonder how is it possible to recognize this impressive number of songs, so quickly? Because of powerful machine learning algorithms. Go figure! Well, this is the short answer. If you want the long story why don’t you read a detailed blog post on this topic?


Google just celebrated the 19th anniversary and it is probably one of the companies which own the biggest amount of data. Due to this aspect, you can observe machine learning effects in every sector powered by Google. From Google Search to Gmail, Google Translate, Google Maps and Google Now, all tools are using different forms of machine learning.


How many of you thought that machine learning is the same thing as artificial intelligence? In fact, machine learning is part of artificial intelligence but it refers to algorithms that help the computer to learn from previous activities. This technology was built for improving the way we use computers and we talked today about its influence in the mobile ecosystem. The conclusion is that we need to do our part and to use machine learning for making our lives a lot easier not to complicate more this world.

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