The term model scaling is particularly at home in the fields of artificial intelligence, big data, smart data and digital transformation. It describes the process of how AI models, i.e. programmes that recognise images or understand text, for example, are adapted to a larger amount of data or a larger number of users.
Imagine working with an AI model that recognises handwritten numbers on letters and automatically digitises them. If it works reliably for 100 letters a day, this is no longer sufficient for a large company with 100,000 letters a day. This is where model scaling comes into play: the model is modified or scaled up so that it can also cope with a much larger volume of data - quickly, precisely and in a resource-saving manner.
Model scaling is important because companies often work with increasing tasks or more data. By scaling the 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 every time.
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