5 Best Ways to apply Machine Learning on your Apps

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Machine Learning is not a new technology. We have already seen many applications with advanced AI algorithms. Recently, when the pandemic hit the human race, there was a rising concern that our hands should not come in regular contact with the face. 

It was essentially pointed out in a post on Reddit when this user wanted to create an app using Machine Learning to alert them whenever his hands came near to face. Though this is just one instance of ML’s application, it is almost an integral part of many mobile apps. 

According to this tweet, 2019 witnessed more funding than any other AI-based technology, which was around $28.5 billion. Mobile apps have seen many new advancements in smart integrations, and ML has one of them.

So, how do you go about leveraging this promising technology? Here, we are going to discuss the five best ways to apply such algorithms on your apps. 

How do I Create an App with Machine Learning?

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ML algorithms can record, analyze, and learn data from diverse sources. These algorithms often do not need human intervention making them useful tools of automation. If you are thinking of creating a mobile app with Machine Learning capabilities, there are two simple ways to do it.

  • Use a pre-built model.
  • Create one for your project. 

There are many pre-built models available in the market that are proven and tested. Some of these models are,

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  • Core ML by Apple
  • TensorFlow Lite by TensorFlow
  • Cloud AI by Google
  • ML Kit by Firebase

If you want to customize your ML algorithm for mobile applications, creating a native app or web app can be fruitful. You can choose to develop native ML apps or web apps now with dedicated kits provided by iOS and Android platforms. 

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Now, let’s discuss the five best ways to use it in mobile apps. 

1. ML for Product Research

Machine Learning in Product Research

Machine Learning algorithms can help you gather and analyze data from the market to evaluate the app idea. Here, you can employ three different ways of researching your product with ML.

1. Generative Research

It is an approach that you can use specifically while entering a new domain. Suppose you are planning to create a chatbot for an eCommerce app that helps customers with product recommendations.

ML helps you beta test a chatbot app prototype with few users to get a general idea of the CX. 

2. Moderated Research

The first user session will provide you with factual data to create usability lists.

You will now know what kind of features customers will like; next, you can have moderated sessions for users and employ an ML algorithm to analyze the feedback. 

3. Unmoderated Research

Create a working prototype of your chatbot app after several iterations of feedback integrations through Machine Learning algorithms.

Now, execute unmoderated working prototype sessions to have feedback on a larger scale before the final product’s grand launch. 

2. ML for App Development

ML in App Development

With the expansion of cloud services and serverless technologies, the integration of Machine Learning into app development is relatively easy. But, the creation of custom ML algorithms needs infrastructure and skilled professionals at the helm.

If you are interested in mobile app development with Machine Learning, you will need a good algorithm designed according to business needs. 

Most of the native platforms offer Software Development Kits or SDKs specifically for Machine Learning. Take an example of the Teachable Machines from Google that allows you to teach your machine image recognitions and other such features.

It is an open-source project for anyone interested in Machine Learning models.

Machine Learning Models and Algorithms

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There are many types of models already used by giants like Netflix, Youtube, eBay, or Amazon, where Machine Learning algorithms offer recommendations to customers based on their usage patterns.

For example, you watch a movie on Youtube, and an ML algorithm will provide you multiple recommendations based on common attributes. 

3. ML for Customer Engagement

Customer Engagement

According to Mihajlo Grbovic, the senior machine learning scientist at Airbnb, 

You would be surprised how many times you interact with a machine learning model when you are on Airbnb.com.

Airbnb is an e-marketplace for users to rent out their houses and spare space for guests. The firm has research sessions with the users to have comprehensive data regarding its product.

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