The term model compression originates from the fields of artificial intelligence, big data and smart data as well as Industry and Factory 4.0. It refers to methods used to reduce the size of large, computationally intensive AI models so that they require less storage space and computing power, but still deliver similarly good results.
In everyday life, model compression becomes important, for example, when AI applications need to run on devices that have little memory - such as smartphones, small sensors in factories or even household appliances. A practical example: originally, a complex image recognition model for error detection in a production line requires a lot of memory and powerful processors. With Model Compression, this AI model can be scaled down so that it runs on a low-cost device directly on the production line and still works reliably.
For companies, this means cost savings on hardware and more flexibility in the use of modern AI technologies. Model Compression therefore makes it possible to utilise the benefits of smart artificial intelligence everywhere, no matter how powerful a device is.
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