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machine learning & AI

In this rapidly changing world of Machine Learning & AI, it is interesting to look at how these technologies can be used to increase efficiency or service in your organization.

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AI comes in many shapes and forms, but the idea behind it consists of the ability for machines (software, in this case) to independently learn, make decisions, and carry out tasks. With it, you can, for example, interpret spoken language (NLP), apply object detection and classification from images or video’s, make valuable estimates based on data (regression – think, for example, about estimating the time needed to complete a task), categorize raw data into groups (clustering), and apply these intelligent methods to concrete tasks such as more efficient programming.


There are various technical options for implementing relatively accessible machine learning & AI. One of the examples is OpenAI's API platform, with their most famous application, ChatGPT. With OpenAI's APIs, it is possible to realize a wide range of AI solutions.

Machine learning

Machine Learning is a subset of AI that uses smart algorithms to produce dynamic models by learning from data, aimed at solving a wide range of problems. The source data that the model uses to learn is therefore incredibly important. For many tasks, such as regression, classification, and some NLP tasks, it is essential that the data is well-structured and labeled to achieve the highest possible output quality.

Data Engineering

Data engineering is a vital support for artificial intelligence (AI). Think of building a house: the AI is the house itself and data engineering is the foundation. Data engineers deal with large amounts of information. They ensure this information is clear and understandable for AI systems. Without this well-organized information, AI systems cannot function properly. So, data engineering helps to make AI better and smarter.

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