The term model compression originates from the fields of Artificial Intelligence, Big Data and Smart Data, as well as industry and Industry 4.0. It refers to methods used to shrink large, computationally intensive AI models so that they require less storage space and processing power, while still delivering similarly good results.
Model compression, for example, becomes important in everyday life when AI applications are intended to run on devices with limited memory – such as smartphones, small sensors in factories, or even household appliances. A practical example: Initially, a complex image recognition model for error detection on a production line requires a lot of memory and powerful processors. With model compression, this AI model can be reduced in size so that it runs on an inexpensive device directly on the production line and still works reliably.
For businesses, this means cost savings on hardware and greater flexibility in deploying modern AI technologies. Model Compression thus makes it possible to harness the benefits of smart artificial intelligence everywhere, regardless of a device's processing power.
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