The term "model scaling" is primarily found in the fields of Artificial Intelligence, Big Data and Smart Data, and Digital Transformation. It describes the process by which AI models, which are programmes that can, for example, recognise images or understand text, are adapted to a larger volume of data or a larger number of users.
Imagine you are working with an AI model that recognises and automatically digitises handwritten numbers on letters. If it works reliably for 100 letters a day, that is no longer sufficient for a large company dealing with 100,000 letters per day. This is where model scaling comes in: the model is modified or enlarged so that it can handle a much larger volume of data – quickly, precisely, and resource-efficiently.
Model scaling is important because companies often work with increasing tasks or more data. Through the scaled use of AI models, processes can be automated, errors reduced, and workflows accelerated. This keeps your company competitive without having to develop a new system each time.
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