Machine learning is easier than ever to learn, although it is very difficult to master. In any case, now there are many ways of integrating machine learning on a commercial app. When talking about machine learning, we’re referring to one of the faces of AI, which employs algorithms and data processing to make sound decisions. Because apps, especially mobile applications, can collect many types of user data, they offer an ideal environment to feed machine learning models with loads of interesting data.
Image recognition
If the app is expected to take or process pictures, it is possible to connect the app with Google’s Cloud Vision API. There’s a free-tier type of account with limited use of the service, which is great for experimenting and learning before connecting Cloud Vision with your app. Another option would be to design and train a model to classify products or images provided by the user.
Nowadays, a trend in web security is submitting the user to a facial recognition test; this can be programmed using the Cloud Vision API. Others use machine learning to automatically fill out database information about retail products, such as new clothing catalogs and food, just by taking a picture of the garment or the product’s label. Image recognition can also become the focus of the app itself.
An app called FaceTune2 has achieved over 10 million downloads by being a smarter version of commonplace photo filters. FaceTune2 will recognize the user’s smile, eyes, hair, and skin, and will let the user employ several tools for smoothing imperfections and obtaining a more polished selfie with very little effort.
Learning about user experience
Netflix is one of the go-to names when it comes to studying user experience. The app is constantly receiving the user’s ratings, schedule, search queries, preferences, language choice and anything that it can pick up. This helps shape business decisions and provides insights into what type of content users are more interested in watching. The thumbnails for each movie and series are also a result of machine learning, as Netflix tailors the thumbnails depending on the user’s apparent preferences. Finally, Netflix also studies bandwidth requirements on each of its regions and time zones, so it can allocate resources more efficiently. Any app can learn from Netflix and become embedded with the foundations for machine learning. One could leave “breadcrumbs” to see where users tend to click and if they’re using the app correctly.
This approach can help provide hints to the users, depending on their behavior, or perhaps the user experience can show that an interface redesign is necessary. Machine learning is not just about building a model; a prerequisite for machine learning is either generating or preparing the data.
Chatbots and natural language processing (NLP)
On a similar note to user experience, Chatbots are a great addition to most business-oriented apps and websites. They can help answer frequently-asked questions without the need of an operator. There are Chatbots based on deep learning, which means that they build upon a large and continuous pool of learning structures; this allows them to become increasingly more capable of handling user requests which at first were impossible to meet. Think of Siri or Alexa; these are no more than chatbots with a great deal of effort and data behind them.
What all of these applications of machine learning have in common is that they are helping the company’s business model. Machine learning can help in several ways, mostly where there is a lot of data to handle. One could classify and streamline the data to make better decisions, such as in the case of Netflix; or one could use a trained model as a way of providing functionalities to a larger system, as it is the case of Cloud Vision and many others. These are just a few ideas to help you figure out if there’s a place for machine learning in your app or business.
