Machine Learning

Machine Learning (ML) is an area of AI dedicated to identifying patterns and relationships in data, and using them to build algorithms that improve automatically as the data stream changes. Machine Learning algorithms are used in a wide variety of applications, often where the complexity of the project or an overwhelming amount of data makes it difficult to use conventional algorithms to perform the desired task.

If you choose to have a solution designed by us, we use a range of computer science and artificial intelligence techniques and tools to ensure the best possible solution to your needs. Our technical expertise covers Advanced Analytics, Machine Learning, Natural Language Processing (NLP) and Computer Vision, and we often combine these techniques to achieve the most effective solution.

Examples of use

Recommendations

A frequently used case for ML and AI is recommendation systems, where algorithms suggest options based on previous data. This is most often used for films, books, experiences and e-commerce products with a view to generating additional sales, but it can also be used internally, for example when proposing treatments for patients based on their symptoms.

Forecasting

It is often useful to be able to predict the future, e.g. in connection with resource planning. Unfortunately, predicting the future is no mean feat, especially when several parameters affect one another. In such situations, ML is a useful tool for uncovering complex relationships and using them for planning and resource optimisation. Forecasting can, for example, be used in Facility Management, creating interplay between route planning of service tasks, energy consumption, personnel and other resources.

Customer segmentation

Many e-commerce websites store large amounts of data about customers, their behaviour and their purchases. This data can be used to learn more about customer segments, and marketing can then be targeted at exactly those segments.

Detecting abnormal events

Identifying deviations in real time is particularly relevant when monitoring heavy processes, where deviations cannot be spotted through standard monitoring. With a sufficiently large amount of data, it is possible to identify what is "normal", and thus also what is not. The method is called anomaly detection, and it works without any previous definition of what normal looks like.

Parameter optimisation

In most applications, dependent parameters emerge that are desirable to optimise: power consumption, time, performance or quality. With the help of ML, agents can be created to control, optimise and keep track of the desired parameters. The application can then be simulated to identify the optimal setting for one or more parameters.

Predictive maintenance

With predictive maintenance, data-driven predictions are used to optimise maintenance, extending the life of the product while minimising the risk of breakdowns. Diverse data sources are often used: changes in energy consumption, revolutions, sound, utilisation rate and input from various sensors. The goal is to provide maintenance or service in a timely manner.

Process automation

ML can be used to automate simple processes, such as filtering and sorting data: anything from documents and emails to reviews and questionnaire results.

Creating an overview and identifying relationships

It can be difficult to obtain an initial overview of large amounts of data. ML algorithms can cluster data to identify patterns and relationships that provide greater insight, for example automatically clustering large amounts of text to identify patterns, giving a better point of departure for understanding the problem.

Curious what this could look like in your business? Call +45 40 60 10 19 and talk directly with our CEO or sales team – or leave a message and we will call you back.