What is Machine Learning and How to Use it in Your Mobile App? (UPDATED 2023)

In an age of digital technologies whose impacts are rapidly transforming the core aspects of our lives, machine learning is emerging as an integral part of this evolution. From autonomous cars to personalized recommendations on your streaming service, machine learning techniques are permeating our modern reality, redefining the way we interact with technology and the world around us.

In our 2017 article, we had mentioned the statistics published by Allied Market Research that predicted the global cognitive computing market to grow to $13.7 billion by 2020 and Fortune Business Insights were estimating that the size of the global machine learning market was estimated at $19.20 billion in 2022. Besides, it is expected to increase from $26.03 billion in 2023 to a level of $225.91 by 2030.

Results such as these just show how much this technology has been growing steadily and what an effect it is leaving on our world. Such forecasts and statistics speak about such enormous opportunities for this technology and so many areas that are being affected constantly by its growth

So, we decided to update this article and make it more useful. In today's topic, we will go deeper in the very meaning of that term “machine learning” and how you can use this machine learning in your application. And at the end of this article, there is a technological stack for you to implement machine learning in your application and also experience of us on that purpose.

Let's start!

🤖 Machine Learning Definition

Machine Learning stands as a subset of artificial intelligence, crafting algorithms through the discovery of concealed patterns within datasets. These algorithms then predict outcomes for novel analogous data, evading the need for explicit coding in each instance.

Conventional Machine Learning merges data with statistical tools, prognosticating outcomes that furnish actionable insights.

The applications of Machine Learning are far-reaching: encompassing image and speech recognition, natural language processing, recommendation systems, fraud detection, portfolio optimization, and automated tasks. This technology is even harnessed to empower autonomous vehicles, drones, and robots, augmenting their adaptability and intelligence in dynamic surroundings.

📱 How You Can Use Machine Learning in Your Mobile App?

Now let's move on to the most interesting. How can we use machine learning in mobile applications?

🤳 Customization: let the user feel special

One of the main benefits of machine learning in mobile apps is personalization. By using machine learning algorithms, you can create a customized and tailored experience for each user. This allows you to offer relevant content, recommendations, and advertisements based on the user's interests and behavior.

Such an individual approach has several advantages:

• it helps users to get the most relevant and alluring content according to their interests;
• your users will feel like your app is really communicating with them;
• posting a targeted advertisement, which is also the part of customization, increases the probability of making a deal.

💵 Machine learning in financial apps: enjoy several advantages at once

Machine learning is also being applied to security and fraud detection in financial apps to protect user data. Another use case is in customer service and assistance, where chatbots and digital assistants can provide users with a more convenient and personalized experience.

⚙️ Tech Stack to use ML in your mobile app

Firebase ML Kit (Google)

Firebase ML Kit from Google offers ready-to-use machine learning APIs that simplify adding intelligence to your app. It includes features like text recognition, face detection, and image labeling, making it an excellent choice for both Android and iOS app developers.

Major benefits: simplicity, cross-platform, support real-time.

Core ML (Apple)

iOS developers can easily include machine learning models into their apps thanks to Apple's Core ML framework. Core ML provides improved privacy, real-time responsiveness, and effective performance since it is tailored for on-device processing and supports a variety of machine learning libraries.

Major benefits: on-device processing, optimized performance, privacy.

TensorFlow Lite (Google)

A powerful framework for implementing machine learning models on Android devices is Google's TensorFlow Lite. It provides a thin and efficient runtime intended to accelerate inference and condense app size. The adaptability and interoperability of TensorFlow Lite make it a popular option for Android developers.

Major benefits: lightweight, cross-platform compatibility, efficient inference.

Azure Cognitive Services (Microsoft)

Microsoft's Azure Cognitive Services provide a suite of pre-built machine learning APIs that enable developers to add advanced capabilities to their mobile apps. These services provide a wide variety of AI functions, ranging from speech and image recognition to language comprehension and emotion identification.

Major benefits: broad range of apis, scalability, ease of use.

IBM Watson (IBM)

An extensive AI platform called IBM Watson provides a variety of tools and services for incorporating machine learning into mobile apps. IBM Watson equips programmers to construct intelligent and user-friendly apps with features like natural language comprehension, image recognition, and predictive analytics.

Major benefits: comprehensive ai platform, customization, enterprise-grade.

Amazon SageMaker (Amazon Web Services)

Amazon SageMaker, part of Amazon Web Services (AWS), is a fully managed machine learning service that allows developers to build, train, and deploy machine learning models. With SageMaker, you can seamlessly integrate ML capabilities into your mobile app hosted on AWS infrastructure.

Major benefits: end-to-end solution, integration with aws, managed service.

H2O.ai

H2O.ai provides an open-source machine learning platform that supports data analysis and model deployment. You can create and implement machine learning models for predictive analytics with H2O.ai, making it appropriate for apps that need data-driven insights.

Major benefits: open-source, predictive analytics, scalability.

📚 Case Study

Participating in our collaboration with LifeBonus, we developed a distinct segment of the application aimed at assisting in elderly care. This feature utilizes artificial intelligence technology to track body movements.

Throughout our efforts, we employed ML Kit for tracking and obtaining coordinates of key points on the body. Subsequently, through a sophisticated analytical process, we evaluate these data points. This feature encompasses around ten exercises. Users will perform these exercises while sitting in front of their phones and receive an effectiveness assessment upon completion.

Looking ahead, we are considering the integration of a "care level" indicator. A higher care level would indicate more complex exercises, signifying a greater need for assistance and supervision. Furthermore, there are plans to train a neural network using this tracking data to autonomously assess users' exercise results.