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How to Make a Machine Learning App [Guide, Based on Our Experience]?

Machine learning app development

The word combination machine learning may still sound like it comes from futuristic sci-fi narratives for many people. You'd be surprised by how often you deal with technologies based on machine learning in everyday life. Facebook's chatbots, Spotify and Netflix suggestions, Amazon’s Alexa, online spellcheckers, and predictive text functions on your phone - these are just a few examples of technologies that use ML.

Market giants across industries have been using machine learning algorithms for quite a while now. In 2017, 500 lines of TensorFlow code (an open-source ML library) replaced no less than 500k lines of non-ML code in the Google Translate app, according to Jeff Dean, the lead of Google AI. The Netflix Recommendation Engine saves the streaming service over $1 billion per year, filtering content based on individual user preferences. Not to mention the funding of machine learning startups – the total investments exceed $60 billion.

Since ML methods entered the global tech market, it has become an essential part of software development. Creating apps that use machine learning is no longer an eccentric idea. This article gives a brief overview of how to build machine learning applications and how your business may benefit from them.

Uses and Benefits of Machine Learning in App Development

Machine learning (ML) is the study of computer algorithms capable of self-improvement over time; this is done by processing huge amounts of data to gather experience. ML algorithms allow artificial intelligence (AI) to develop pattern recognition mechanisms, continuously learn from predictions, generate data-based predictions, and make appropriate adjustments without any additional programming. In simple words, ML models find patterns in huge amounts of raw data and make the best decisions without human participation. 

This technology can enhance any mobile application by providing a new user experience level, whether it’s offering the most accurate media recommendations based on the user's activity or instantly detecting diseases in plants. Many fields of human activity considerably benefit from ML-based technologies. 


In the mid-twentieth century, mathematicians developed algorithms for understanding and predicting financial market behavior. Now it's done by machines, which makes the process significantly more effective. Besides technologies that predict stock exchange fluctuations, there are AI-powered financial assistants or banking risk assessment systems that generate customer reliability reports.

Mastercard implemented the Vocalink solution to prevent fraud at early stages. It monitors all transactions and detects suspicious activities while also providing analytical reports and preventing money laundering.


Today's healthcare system uses equipment that generates a lot of data. By working with this data, self-learning programs can find more effective treatment methods, get a diagnosis faster, control patients' conditions, or prevent medical crises.

Healthcare App ML

Condition-based apps help users track diabetes, epilepsy, or heart diseases. Let's not forget about fitness apps and nutrition planners.

Microsoft introduced their 3D processing technology, Project InnerEye, for radiological images. The ML-powered technology differentiates between healthy physiological objects and tumors using 3D radiological imaging and assists medical experts in surgical planning and radiotherapy.


Machine learning has long been actively implemented in e-commerce services. Special algorithms analyze purchase history, age and social groups, preferences, and plenty of other parameters to develop personalized offers. Fraud detection and prevention, trend forecasting, and analytics - there are many ways of ML model implementation.

Granify is an ML-based revenue optimization and customer conversion technology. It helps retailers monitor their customers' behavior and make predictions on potential visitor-to-buyer conversion. It also assists in building business strategies to achieve these conversions.

Law Enforcement and Security

Deep learning algorithms are extremely effective in face recognition systems development. These technologies use different models to detect faces, create so-called face maps, and then compare them to existing databases. Law enforcement agencies run databases to solve and prevent crime and use these technologies in counter-terrorism and anti-human trafficking operations. Private companies have sophisticated internal security ML-based systems focused on employee face recognition, data leak detection, or security breach prevention.

Many organizations rely on User and Entity Analytics (UEBA) tools in their security policies. These systems can learn usual employee behavioral patterns and detect suspicious activities. The technology provides many helpful features; e.g., it is useful for preventing insider attacks. 

Transportation and Logistics

ML algorithms are used in the internal systems of many transport and logistics companies. The simplest feature is continuous updates about traffic conditions. These apps optimize the route to ensure on-time deliveries or avoid extra fuel consumption, analyzing up-to-date information. Moreover, by processing historical data, these technologies learn to predict traffic conditions.

The retail giant Amazon operates 200k robots in the company’s warehouses. Amazon Robotics has a tremendous impact on logistics processing – robots assist humans in sorting, packing, transporting, storing, and tracking goods.

Feeling inspired? We are here to show you what your mobile project is really capable of! 

Types of Machine Learning Algorithms for Mobile Apps

Before creating enterprise apps that use machine learning, you need to set up your objectives and choose an appropriate algorithm. Machine learning methods are sets of processes aimed at performing specific tasks in a particular environment. There are many types of ML algorithms, and the choice depends on the project's objectives. Here are some facts about the three main methods.

Supervised Learning

This method uses already labeled data. The algorithm learns from the past input data (training data), analyzes it, and uses the analysis to predict new data behavior within known classifications. The exact prediction requires large amounts of data. The algorithm can be further trained by comparing the training results with the actual ones and using errors to modify the algorithms. Some examples: 

  • Spam detection (Mailwasher, ORF Fusion, Spam Titan, ESET Endpoint Security)
  • Place-targeted advertising (HubSpot, BrightEdge, Criteo)
  • Market predictions (AiAlpha, Personae, Stocksight, Gekko Strategies)
  • Face recognition (Facebook’s DeepFace, Google’s FaceNet, the Megvii’s algorithm)
Face recognition Mobile App

Unsupervised learning

This method uses unlabeled data. The algorithm receives large amounts of data and defines its features and correlations. It also learns to group and organize the data so it can be understood and interpreted. Often, this model comes up with non-evident solutions. Some examples: 

  • Recommendation systems and analysis of consumer preferences (Spotify, Netflix, Amazon, LinkedIn, Facebook)

Reinforcement Learning

This method is aimed at more complex tasks and includes interaction with the environment. One can describe it as a reward and punishment method. Here, the algorithm receives either positive or negative signals based on its correct and incorrect decisions. Some examples: 

  • Machine translation (Reverso, Systran, Smartcat, Matecat)
  • Resource management (Glint, PhenomPeople, Workday)
  • Robotics (Amazon Robotics, MICO robotic arm, Watch-bot, RoboWatch)

How to Build Machine Learning Applications

Specialists build a machine learning app using particular procedures that include the following steps.

Step One: Problem Framing

First, you need to define a machine learning task. It means outlining the predictions and the kind of data required to make such predictions. The predictions are usually target answers or labels. They can represent yes/no labels as in binary classification, categories as in multiclass classification, or real numbers as in regression.

Step Two: Data Collection and Filtering

The next step is collecting data from either open datasets, historical databases, or any other sources. It's important to organize and filter data, which includes removing duplicates and correcting errors like missing values. The data should be randomized as a specific order may affect its processing. 

Data collection instruments in machine learning are usually divided into four main types.

  • Open-source datasets are easy to reach, as there are thousands of them available on the Internet. It’s the best approach if you don’t have enough internal data. Usually, open-source datasets are already structured and ready for use.
  • Scraping web data is an automatic data extraction process based on easy to implement algorithms. This approach is highly popular in building machine learning applications, as it’s a multi-purpose instrument suitable for various projects – researchers, individuals, governments, and businesses use these tools.  
  • Synthetic datasets serve as a solution when there’s not enough real-world data available. In this case, you can use programmatically generated data to obtain enough datasets to train your neural networks. Synthetic datasets also eliminate potential copyright or privacy issues.
  • Internal data is the choice of large organizations. It provides the most accurate and relevant results if you have enough data from your internal systems. However, this approach may be controversial if you process customers’ private information.

Step Three: Data Preparation

You need to ensure that any dataset piece can be uploaded inside the ML algorithm correctly and efficiently. You might need to change the data format or size for media files or format and encode text data. 

There are hundreds of options for data filtering and preparation, depending on your knowledge, experience, and resources available. It can be well-known data processing software like Microsoft Excel or SPSS, specialized machine learning tools like Paxata, Trifacta, Alrteryx, Databricks, Apache Hive, or purely mathematical calculations through programming code.

Step Four: Feature Engineering

Feature engineering is the process of designing the expected features from existing data for machine learning. This step sometimes can be more important than the actual model you use, as ML algorithms learn only from the data you provide. You need to make sure the algorithm has accurate data interpretation.  

Step Five: Model Training

At this stage, the data should also be split into training and evaluation sets to monitor how well the model processes unknown information. You provide the algorithm with labeled datasets that already contain correct solutions. This way, the algorithm will learn patterns and correlations between already labeled and unknown data. Often, besides obtaining correct answers, the objective is to improve the results.

There are two ways to perform the training process - training from scratch and model fine-tuning. The first option is for companies facing unique problems when no ready solution is available. This approach requires engaging highly skilled data scientists, which makes this scenario time- and budget-consuming. 

However, multiple open-source projects may be the alternative to creating enterprise apps that use machine learning. They provide a variety of already existing models that are likely to fit your project's objectives. In this case, you can fine-tune such an existing model to adapt to your data and tasks. The fine-tuning approach usually takes less time and is less resource-demanding. 

Step Six: Accuracy Evaluation and Improvements

At this stage, you can measure how accurate the model's performance is when processing an unknown validation set. There are different scenarios for performance problems and solutions to each scenario.

Step Seven: Real-World Prediction

The purpose of the training process is to teach your algorithm to make accurate predictions on unknown data. Once the training phase is completed, the algorithm has all the necessary experience to analyze unlabeled datasets. This is the most important stage for your project's business implementation. At this stage, the model should be ready to process real-world information input and provide reliable results.

We keep up with all information about Machine Learning and AI, and our digital product development team knows how to make these technologies work for you. Do not hesitate to drop up us a message with your question.

Machine Learning App Cost

How much will it cost to go through all the steps? Let’s have a look at the main parts of any ML project: data and data scientists. To make it more practical, imagine you have to solve an image classification task, and image labeling is needed. According to Google AI Platform, labeling on 1,000 images will cost $35 per human labeler. For example, to solve your business task, 100,000 images are needed. To make labeling quicker, you will attract 20 labelers, and this work will cost you $3,500 (without Tier 2 preferences). 

According to PayScale, Data Scientist will cost you $96,000 per year on average. If everything goes well: the whole task will be finished within one month, and you don’t need additional data — the entire project will cost you about $11,500. It should be noticed that in bigger projects, you need to hire more specialists and label more data.

Want to get an estimate of your mobile project? Fill out the form.

Why Is Machine Learning Important in Mobile App Development?

We've already learned about the main benefits of ML technologies for different fields of human activity. There's more to it. With machine learning, there's no longer a need to write extensive instructions that consider all possible problems and their solutions. Instead, the algorithm uses statistical data to outline patterns and make predictions. It optimizes and automates the decision-making process.

creating apps that use machine learning

Very likely, in the near future, ML algorithms could become an essential part of the software development process. Here are some reasons to create a machine learning app.


All the passwords you use to log in to social network profiles and email accounts, or just to unlock your mobile device, can be compromised. Even amateur hackers are able to easily pick your password and get access to your personal data. Face and voice recognition algorithms can be an additional layer of protection to keep your data safe. Due to constant technological progress, developers nowadays have easier access to ML-powered security algorithms. Two examples are FaceID and TouchID authentication mechanisms by Apple.

There are also ML-powered anti-virus systems, known as endpoint detection and response models. They can be used on the server side of the application. Such models learn usual system activity patterns to detect any abnormalities. Besides that, these models are able to filter uploaded files, email attachments and monitor web activity. Another important point to consider is the security of the ML application development process. The main goal of this protective measure is to prevent data leakage. This is especially important when internal or client personal data is used for model training.

User Engagement

Information categorization, customized suggestions, personalized offers - these features help to keep users interested in your product and its updates.

User Behavior Analysis

When implemented into your product, ML algorithms work with large amounts of data provided by your customers. Knowing who they are and what they want is the key to a successful business model. This way, you don't have to incorporate external resources to gather and analyze customer statistics.


Machine learning is a powerful data processing and analysis technology that can become a powerful tool in your business development strategy. It's based on the idea that the machine can be trained without or with a minimum of human participation. Creating apps that use machine learning is extremely beneficial in many business and production fields.

However, once you decide to build a machine learning app, you will likely require professional machine learning development services - an experienced team of professionals that can guide your project at all stages.

Whether your company requires predictive algorithms for user behavior analysis, business intelligence for data management and automated decision-making, or big data analysis - these projects should be entrusted to experts.

If you need help with identifying the right approach for your project

contact us to get consulted on this question

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