Close this search box.

Machine Learning Mobile Apps

Machine Learning Mobile Apps

Machine Learning Mobile Apps: A Primer

Learning is a universal skill possessed by every creature on this planet. Machines can also learn by recognizing data patterns and applying specific rules (algorithms) to similar situations. This process is known as Machine learning. Machine learning is a subset of Artificial Intelligence (AI), which is human intelligence exhibited by machines. The machine learning process of training a computer contains the following constituent steps:

  • Input new or old data.
  • Analyze the data. 
  • Identify patterns in the data.
  • Predict meaning or make a decision based on the data patterns.
  • Record the feedback and use it to be more accurate when faced with similar input. 

Benefits of Machine Learning Mobile Apps

AI and machine learning have disrupted many industries. Machine learning mobile apps, in particular, are in wide use today. Algorithms in machine-learning apps can scan user data and quickly infer preferences, behavior, pain points, constraints, likes, and dislikes. This data is used for customer segmentation and targeting with specific features and offers. Hence, personalization is undoubtedly the most significant impact of mobile machine learning. 

Apps that use machine learning also make use of intelligent automation to infuse efficiency and productivity in businesses. For example, many companies now run their chat support using chatbots. Chatbots are pre-programmed to be interactive and answer common support questions. Where the chatbot cannot provide an answer, the query is sent to a real human. These Machine Learning apps or ML apps have made it possible for companies to offer round-the-clock customer service. Overall, they have led to massive improvements in customer service metrics. 

How to Get Started with Machine Learning Mobile Apps

If you’re a business trying to figure out how to implement machine-learning apps for your business, it can seem daunting at first. Below, we provide you with the key aspects you should consider when thinking about getting started with machine learning mobile apps. 

Are You Machine Learning Ready?

You should start by considering whether you have all the prerequisites to begin building an ML app. Consider the following:

  • What problem are you trying to solve: This is probably one of the most common mistakes made by businesses. When technology is going through a hype phase, the knee-jerk reaction by most business managers is to do what everyone else is doing without carefully considering if the technology will solve a problem. What mission-critical problems exist in your business that a machine learning app would help you to solve? This will ensure you don’t build useless ML apps that don’t solve a problem for your company. 
  • Data: Do you have good quality data that are valid, accurate, and complete? Do you own or collect large volumes of data? And, can you access and process the dataset at scale? These three factors are crucial to building successful ML apps. If any of these ingredients is missing, you have to improve your data collection and management infrastructure. 
  • Skills: Do you have data scientists and data engineers on your team to execute the project? If you don’t, you may have to hire qualified people or train existing team members. Online training resources are abundant if you chose to go down this route. 

Choosing Your Machine Learning Model

Machine learning models can solve a variety of tasks depending on your needs. The model you choose will depend on whether your data is structured or unstructured. 

Structured data exists in a fixed field within a file or a record. The format is typically rows and columns. It is usually stored in a relational database and consists of numbers or text. 

Unstructured data is everything else. It is stored in its native format and is not predefined. Most enterprise data consists of unstructured data. Think text, video and images, social media activity, mobile data, email messages, business apps, scientific data, surveillance footage, sensor data, and so on. 

Some data overlap these two main categories and are defined as semi-structured or quasi-structured. 

Below are the recommended ML models for structured data:

  • Classification: These models are ideal for image classification and language detection in mobile applications. 
  • Regression: Regression models are great for forecasting, churn analysis, and fraud detection applications. 

Below are the recommended ML models for unstructured data:

  • Clustering: These models are ideal for product cross-selling and content recommendation applications. 
  • Association analysis: Association analysis models work well for customer segmentation and ad targeting models. 
  • Reinforcement learning: These models are ideal for self-driving vehicles and gaming applications. 

Choose the Approach That Best Fits Your Requirements

The approach you choose depends on your expertise, dataset, and budget. The illustration below represents the spectrum that guides your approach. 

Machine Learning Mobile Apps

On the extreme left, you have application developers who want to use production-ready machine learning models. There are hundreds of ready-to-use open-source machine learning software libraries. This approach is ideal when you have low machine learning expertise and limited training data.

In the middle of the spectrum, you have data scientists who want to easily train sophisticated ML models on their data to drive both real-time and batch predictions. 

On the extreme right, you have machine learning experts who want to train their models. This approach is ideal where you have high machine learning expertise and a large data training set. 

Choose the Best Framework to Run Your Model

Machine learning runs on complex algorithms that prove to be a steep learning curve unless you are a data scientist or machine learning expert. Machine learning frameworks simplify the development of machine learning mobile apps. Essentially, the framework allows you to build the model without learning how the underlying algorithms work. Different frameworks are geared for different purposes. Below are examples of machine learning frameworks to run your model.

  • TensorFlow: Google Brain developed TensorFlow before making it open source. This feature makes it is the most popular machine learning model. It supports multiple platforms, and processing units and models can be run directly on mobile with TensorFlow lite. TensorFlow has ready-made ML software libraries, can be used to train sophisticated models and build custom models. 
  • PyTorch: This framework is the main competitor to TensorFlow and was developed by Facebook AI Research. For most businesses looking to develop a machine learning mobile app, the decision comes down to a choice between TensorFlow and PyTorch. 
  • Scikit-Learn: This framework is ideal for quick tests to measure the success of a hypothesis. It is a great tool to build a quick prototype or to develop a proof-of-concept. 
  • Google Cloud Machine Learning API: This is a cloud vision, speech, translation, and natural language application programming interface (API). 
  • Spark ML: Spark is ideal for working with large arrays of data. It distributes the workload to different servers, thus ensuring that your computer doesn’t run out of memory. However, it is complicated to work with and is best suited to ML experts. 
  • Torch: This is one of the most accessible frameworks to use. The simplicity is attributed to the Lua Programming Language interface. 

Model Constraints

One of the constraints to building a machine learning mobile app is the size. To retain a great user experience, the app has to be nimble, which means it must have a reasonable small size to run smoothly on the majority of devices. 

The other constraints that developers must deal with are the battery life of mobile devices. Resource hungry machine learning mobile apps can quickly drain batteries. 

Examples of Successful Machine Learning Mobile Apps

Artificial intelligence and machine learning have given the world stunning mobile applications. This has led to huge investor interest and a healthy flow of venture capital funding. Investments in machine learning are expected to reach $58 billion in 2021 and market growth at an annual rate of 44.06%. Tangible results fuel this growth. Below are examples of some of the most successful machine learning mobile apps. 

  • Google Maps: This service has revolutionized the transport and logistics industries. Using training models to aggregate location data, the app can make predictions such as traffic and provide users with routing options. 
  • Snapchat: When it was first launched, Snapchat’s face detection abilities wowed audiences. The application uses advanced proprietary ML models to train and recognize faces. 
  • Netflix: This popular Over-the-top (OTT) service uses regression and clustering to categorize content into various genres, reviews, actors, and years, as well as provide viewing recommendations based on past watching habits. 
  • Tinder: Tinder uses machine learning to match people to their soul mates. By swiping right to like and left to continue with the search, the app learns what traits the user likes and uses this information to find compatible matches. 
  • Oval Money: This financial planning and coaching application analyzes spending habits and transaction data to provide users with practical money-saving tips. 

The Future of Machine Learning Mobile Apps

The next generation of machine learning mobile applications will be smarter and more powerful—some of the areas where we are bound to see interesting developments. One area is that of news applications. The spread of fake news and misinformation has led people not to trust conventional news sources, blogs, and social media. New mobile applications are being developed that will use machine learning to distinguish fake news and bury it in search results or notify users. 

Machine Learning in Analytics
data science to grow business
What are progressive we apps

Explore our topics