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September 01, 2022

Machine Learning. What it is and why it is essential to business?


Kaja Grzybowska

Reading time:

13 minutes

AI – praised and terrifying at the same time – is already here, scanning our emails, listening to our convos, recognizing our faces, predicting future events, and detecting potential disruptions. It is becoming a must-have for any business innovation, regardless of the industry or niche, and Machine Learning seems to be a starting point to do the magic.

Now, it is all about data that is considered a “now oil” that accelerates business growth and increases the performance of any organization. And yes, we are aware that in the time of self-driving vehicles, intelligent assistants, and Black Mirror-reflected fears, that statement may sound a bit corny, but – in real life – the whole spectrum of AI applications requires clarifications.

Artificial Intelligence is a broad umbrella term covering the variety of subsets of AI-related approaches. Machine Learning is one the most basic (even though “basic” may not be a proper word to describe its potential).

So, first things first: what is machine learning?

What is Machine Learning?

Machine Learning (ML) can be seen as a subset of Artificial Intelligence that allows systems to improve themselves without being explicitly programmed. It focuses on features that can access data and use it to look for patterns and make better decisions.

AI is a more significant concept. It is about the whole methods of building systems that enable machines to simulate human thinking capability and behavior. There is a difference between these two terms, but it is no surprise that non-techies use them interchangeably.

What is machine learning?

Machine Learning (ML) enables computers to “think” and learn like humans, basing their conclusions and future predictions on historical and real-time data analysis.

Machine Learning consulting opens up numerous new opportunities for your company. With Machine Learning algorithms, you can:

  • personalize customer service,
  • automate processes,
  • implement solutions that will change how customers interact with your product.

Other artificial intelligence subsets are:

  • Natural language processing (NLP)

Natural language processing (NLP) is an area of ML in which machines learn to understand the natural language spoken and written (instead of the numerical data). NLP is the heart and soul of chatbots and intelligent assistants such as Siri or Alexa.

  • Neural networks

Neural networks mimic the way the human brain works, in which numerous “nodes” are related to each other and – overlapping each other – create new sets and subsets.

  • Deep Learning (DL)

Deep Learning (DL) is a subset of machine learning that puts stress on neural nets. It allows machines to solve very complex problems even when using a data set that is very diverse, unstructured, and incomplete.

How machine learning works?

Machine Learning algorithms start, obviously, with data. That can be numbers, photos, text, pictures, repair records, data from sensors, sales or marketing reports, etc.

how machine learning works

Data is collected and prepared to make them useable as training data to “feed” the Machine Learning model with “knowledge.” The more data, the better the program will be. Developers can choose the specific Machine Learning model to use, provide it with data, and let it train itself to find patterns or make predictions. Yet, it doesn’t mean that their work is done. They can improve the model at any time, optimizing it, changing its parameters, and constantly forcing it to deliver more accurate results.

There are several kinds of Machine Learning models, all depending on the data they are trained with.

On the most basic level, we can establish three primary subcategories of Machine Learning:

  • Supervised ML model

Program is trained with data labeled by humans.

Supervised Machine Learning is typically used when we arbitrarily know what kind of outcome is needed. It requires providing ML models with a vast set of training labeled data to make them examine the output and set the parameters until getting the desired results.

Those models are widely used in making classifications and predictions. They can use historical data and – based on them – identify the risks, potential disruptions, anomalies, anticipate users’ behavior, future trends, etc.

  • Unsupervised ML model

Program looks for patterns in unlabeled and unclassified data.

Unsupervised learning works with unlabelled data, enabling computers to explore a data set and identify hidden patterns within it. They detect connections between variables and turn data into groups or categories based only on statistical properties.

Unsupervised learning – as opposed to supervised learning – doesn’t require training on large data sets, so it is faster and easier to implement. It is useful, especially in clustering related products or items for smart recommendation engines, identifying anomalous activities, and detecting emotional sentiment in texts.

types of machine learning

  • Reinforcement ML model

The program is trained by a trial and error method and rewarded when the result is accurate or the right decision was made.

Reinforcement Machine Learning models are based on repeatedly running many possible scenarios to make the model learn from its own experience. The model is more sophisticated and can be applied to perform complex behavioral tasks and solve optimization problems.

Developers’ efforts are focused on setting the reward policy, but there are no suggestions on how the model is supposed to achieve success. The model starts making random trials and repeats them for as long as it is needed to gain the reward, simulating the human way of learning. However, contrary to humans, it can tirelessly run thousands of parallel scenarios and gather more knowledge.

What are the main benefits of using Machine Learning?

Machine Learning models process cast amounts of data and identify patterns that improve business decision-making at all levels. Moreover, they are updating by themselves and improving their analytical accuracy each time, which makes the investment in ML highly efficient: one-time expense brings growing benefits from day one.

main benefits of machine learning

From manufacturing to retail and banking to bakeries, even legacy companies use Machine Learning algorithms to unlock new value or boost efficiency.

“Machine learning is changing, or will change, every industry, and leaders need to understand the basic principles, the potential, and the limitations.”

– MIT computer science professor Aleksander Madry, the MIT Center for Deployable Machine Learning director.

Although we are far from calling Artificial Intelligence, Machine Learning, or Deep Learning a remedy for all kinds of business challenges, it is market-proven that the ML models can solve various problems daily. As an “industry-agnostic” technology, it can benefit organizations of all types and sizes.

Speeding up the decision-making process

Machine Learning’s main application is to speed up decision-making, allowing businesses to process and analyze data more quickly than ever before. Still, its business goal is to use them to improve overall performance. Machine Learning algorithms enables the business to turn them into data-driven organizations by giving the managers tools to make smart, data-grounded decisions in a snap. It can detect anomalies, data breaches, and security disruptions and notify specific departments that taking action is needed in a way that is not available with manual handled processes.

Forecasting demand more accurately

Machine Learning, with its capacity to analyze internal and external trends, supports predicting trends and anticipating customer behaviors. It enables companies to keep up with customers’ needs, which leads to more effective inventory management and significant money savings.


Personalization has recently become one of the most crucial factors influencing customers’ purchase decisions. Customers overwhelmed with offers coming from multiple platforms and channels started to demand special treatment that would save them time; they don’t want to browse endlessly, scanning and comparing offers – the convenience that comes with personalization is what they demand from companies. With Machine Learning models that analyze user behavior and suggest cross- and upselling strategies, retailers drive more sales.

Improving efficiency

Machine Learning models accelerate repetitive, mundane tasks and shift human resources to higher-level, strategic activities. That reduces costs for information retrieval activities related to regulatory compliance, legal research, etc., while freeing employees to focus their efforts on overall strategic planning.

Are there any Machine Learning drawbacks?

The market proves the benefits of implementing Machine Learning models, but – to fully cover the issue – it is good to pinpoint the challenges and potential problems this implementation can imply. And there are no surprises.

As we said above, Machine Learning algorithms reveal their potential with time due to exposure to valuable data sets. Thus, there may be a period when the algorithm is not developed enough to fulfill your needs.

Also, the key to Machine Learning efficiency is data, so – on the one hand – handling them demands a lot of computing power, which can potentially be quite costly, and – on the other – the quality of data is crucial.

Given all of this, it’s essential to consider whether you can invest the right amount of time and money in developing the technology to a point where it will be helpful and – if you have valid data to “feed” it properly.

The following challenge that goes with Machine Learning models is that they cannot supervise themselves. When the machine is kicked off, the flow is just running, and its results are dictated only by the quality of the data, so – if it has been mislabeled or is flawed in any other way – the system will not detect the error, and the results will be skewed.

Thus, on the one hand, Machine Learning, thanks to its autonomous nature, severely reduced the need for human input; on the other, however, it requires human supervision. And that’s problematic because typically companies implement Machine Learning models to go fully automated.

Which industries can benefit from machine learning?


Machine learning enables computers to “think” and learn like humans, basing their conclusions and future predictions on analysis of historical data and real-time data.

The global Machine Learning market in manufacturing is expected to reach $16 billion by 2025. Manufacturing companies invest, among other things, in process automation and reduction of operating costs.

Machine Learning algorithms enable companies to take full advantage of the data.

We’ve helped a leading manufacturing company to enhance management efficiency by implementing an automated reporting system and Data Warehouse, which covered HR, operations, business, and sales processes.

Read our article about Machine Learning In Manufacturing.

Which industries can benefit from ml?


Machine Learning and AI tools can significantly impact various aspects of the gaming sector. Machine Learning algorithms can dynamically respond to player actions. AI tools such as chatbots can significantly assist players, eliminating waiting for an answer and providing suitable information.

We helped a mobile gaming company to increase IAP, LTV, and retention using artificial intelligence-driven technology. We implemented analytics in mobile gaming, transformed big data into an appropriate format, implemented fraud detection, and applied machine learning-based tools, enabling our client to predict customer lifetime value (CLV) and customer churn. Through Modern Data Warehouse and BI implementation, we achieved data monetization.

Learn more about our innovative solutions from our case studies – mobile gaming.


eCommerce – is one of the first industries that started using all the benefits of Machine Learning. Recommendation engines and machine learning in the eCommerce industry directly convert into profits and increase companies’ market share with better customer acquisition.

Machine Learning solutions help eCommerce businesses to improve almost every part of their operations – from inventory management to customer service. Zalando, Asos, and Amazon, companies that are the most recognizable names in the eCommerce industry, have entire Artificial Intelligence (AI and Deep Learning departments).

They invest tons of money to have better knowledge of their clients, personalize offers for a particular customer, improve customer experience and automate manual processes.

Addepto machine learning consulting team has analyzed which solutions have the biggest potential today. They can help monetize your data and improve customer experiences like Asos and Zalando.

Read the article The Best Machine Learning Use Cases In E-commerce.


Machine learning in the logistics industry analyzes data sets and looks for better ways to handle processes such as improving demand forecasting or demand accuracy, inventory or route optimization, supply planning, warehouse management, calculating risk, and so on. Using Machine Learning, the amount of human labor can be significantly reduced, saving time and money.

We’ve helped a logistics company make the right business-critical decisions using full information. We integrated data from multiple systems (ETL) into Enterprise Data Warehouse and implemented predictive models on top of that. The logistics route was optimized, and the right business decisions were made thanks to deployed analytics systems.

Learn more about our innovative solutions from one of our case studies.


The financial sector was not the most agile and eager to jump on the latest trends as soon as they emerged, but where money is, there is competition, and where there is competition, there is a continuous arms race. And now, the actual battlefield is technology, which touches all aspects of financial operations – on the frontline and back. One of the crucial technologies driving the rapid evolution of the financial sector is artificial intelligence (AI) – especially Machine Learning – that enhances fraud detection, high-frequency trading, risk management, investment management, and so on.

Machine learning and AI play key roles in today’s finance industry. Let’s examine some of the recent ML applications in finance.

Learn more about our innovative ML implementation in finance.

Machine Learning Consulting

Reliable data analysis and reporting will lead you to faster and more consistent development, but they require delivery and support from an experienced partner. This is where Addepto comes in.

We will help you enter the data-driven world and modernize your existing business analysis systems. We will help you extract insight from the collected data and turn it into profits and insights.

How we work on Machine Learning solutions?

Business meetings & data analysis

We start our collaboration with open discussions about our customer’s needs and goals. We do our best to understand how an organization operates and which machine learning solution would be most helpful for our client.

machine learning solutions

The next step is data analysis. Once granted access to necessary data, we use ML techniques to process it and determine which data sets will be crucial for implementation processes.

Technologies that help us build our Machine Learning solutions

Python is programming; a general-purpose language often used to build websites and software, automate tasks, and conduct data analysis.

Amazon Kinesis is an Amazon Web Service (AWS) for processing big data in real-time, processing the vast amount of streaming data from multiple sources.

R is an open-source scripting language for predictive analytics and data visualization.

technologies that we use to build ML solutions

MongoDB is an open-source NoSQL database management program that manages document-oriented information and stores or retrieves information.

PyTorch is an open-source Machine Learning framework that accelerates the path from research prototyping to production deployment.

TensorFlow is a Python-friendly open-source library that fastens the Machine Learning models’ deployment.

Apache Hadoop is a framework that enables developers to distribute the processing of large data sets across clusters using simple programming models.

PySpark is the Python API for Apache Spark.

We build Machine Learning solutions for customer predictive analytics, predictive maintenance, computer vision, text processing, and full-stack BI or Big Data implementation.

We encourage you to download and share our infographic: 



Machine Learning