How To Write Killer Machine Learning Apps

The smartphone revolution is upon us and what a coup it’s shaping up to be. More than 2.5 billion people, about 36% of the world’s population, own a smartphone, a massive improvement from only 10% in 2011 and the best is still yet to come. It is, therefore, no surprise that mobile app development is one of the most lucrative gigs in the tech industry, with thousands of young, creative developers raking in thousands of dollars per month.

With millions of applications covering every aspect of life available in Apple, Windows and Android stores, the last thing you want to do is create another generic app. Right now, the demand for “smart” apps, typically build around Machine Learning concept, is continually increasing and so are the potential returns. Unsurprisingly, the most popular apps right now have some Machine Learning features including Instagram, Netflix, and Snapchat. But before going any further, let’s clear up some things.

What is Machine Learning?

A branch of Artificial Intelligence, Machine Learning is the art (yes!) and science of teaching computers to analyze patterns and data given, learn from them and react appropriately, just like humans do. Unlike standard apps, Machine Learning apps are not based on hard coding or programming rules but learn from examples and develop their own way to perform tasks.

In the case of Netflix, for example, algorithms pick up the kind of movies you like, based on genre, production house or any other variable and thereby put similar ones in your recommendations. YouTube also uses a similar approach which explains why your visits always last longer than you intend them to.

That said, we are cognizant of the fact that different operating systems have different requirements and methods of app development. As such, our guide is divided into 3 parts, covering tips for writing killer Machine Learning apps for Android, Windows, and iOS in that order.

Building Machine Learning Apps For Android

Android devices are relatively cheaper than Apple or Windows devices, making them a favorite of most people. Creating an Android app that catches on can catapult you into stardom and several tax brackets above. But let’s not get ahead of ourselves here.

Anyway, the Google Play Services Software Development Kit (SDK) offers some features and APIs to assist developers in integrating ML in their applications including, but not limited to cloud storage space to save on bandwidth. Through the ML Kit package, you can have access to APIs and programs like:

• Mobile Vision – Allows you to build apps that can detect and scan faces, text, and barcodes in videos and images. Your apps will also be able to use Android cameras to do these tasks.
• Text Recognition – With this API, you can create apps with the ability to detect text in images and videos. It can recognize all the most-spoken languages such as English, Spanish, German and Portuguese segments identified text into words, lines, and blocks for easy recognition.
• FaceDetector – Helps you develop apps to track and detect faces using the camera function. The API scans facial features such as smiles, noses, and eyes while matching faces and can scan multiple faces in a single frame. Notably, the current FaceDetector version only works when users have their eyes open which may or may not be convenient for you, depending on your development plans.

The Android ML Kit contains hundreds of useful and free APIs that you might want to check out if you intend to work on compatible apps. We have just listed a few that are widely used, particularly in creating social apps which are the craze of our time.

Machine Learning Apps For Windows

Microsoft does not offer much regarding APIs, but as Lori Wade, the chief of Edubirdies, says, it’s not what you have that counts, but whether you make it count. The Microsoft Windows 10 software consists of several tools and features that enable you to use pre-trained Machine Learning models in your development process. These include:

• Azure Machine Learning Studio – An open source program that offers drag-and-drop functionality, allowing you to build, test and export Machine Learning functions to your apps. Also hosts numerous APIs and programs including editing and cloud storage facilities.
• Content Moderator – A must-have API when creating social applications, Content Moderator offers both automated and manual content moderation and review functionality. With the tool, your apps can; detect offensive images and videos and allow users to moderate and block said content.
• Microsoft Translator API – Gives apps the ability to instantly translate text to various language, made even easier by the new Neural Machine Translation (NMT) system that leads to more intelligible translations. It is an excellent choice to use when building apps for the international audience which should be your goal anyway.

Microsoft also supports the Open Neural Network Exchange (ONNX) framework that facilitates sharing of Machine Learning models and allows cross-platform integration. You can build, train and employ neural networks to train your models and then transfer the algorithms to another framework or your app after they have learned.

Machine Learning Apps For iOS

If you follow, you might have seen the new iPhone X launch and heard something about FaceID and Augmented Reality, new features that are based on Machine Learning. They were a bit late to the party, but Apple recently dipped their hands in the ML pool with the CoreML SDK. The kit presents developers with fantastic features such as a faster GPU, Natural Language Processing (NLP) functionality and APIs for, among others, image and video analysis, face and text detection. For gaming apps, CoreML enables access to GameplayKit, a game development library that provides both AI and ML integration features like random forests and decision trees.

The CoreML kit uses Python 2.7 language which you can download from Python.org website. Also, in case you haven’t built Apple apps before, you will need the Xcode 9 software, the licensed software for Apple app development. The software, together with Apple Developer, contain helpful tutorials and guides on how to go about creating different types of ML models and training techniques.

Conclusion

The opportunities for integration of Machine Learning and Artificial Intelligence as new technologies in app development are endless and will be for a long time. However, a lot of developers are afraid to venture into the area for one reason or another but, mainly, due to lack of experience or knowledge. Our aim is to initiate discussions and probably spark fires in our readers’ minds that will hopefully inspire them to create some fire Machine Learning apps. By the way, you don’t have to start coding an ML application from scratch. You can begin by using some of the APIs and programs mentioned above to improve your existing app incrementally. You can, for instance, add a speech recognition feature to your mobile app or even a translation function to boost user experience. The old folks didn’t lie when they proposed that practice makes perfect.

 

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