Model pruning is a term from the fields of Artificial Intelligence, Big Data and Smart Data, as well as Industry and Industry 4.0. It describes a method for \u201eslimming down\u201c artificial intelligence models, such as neural networks. Parts of the model that contribute little to accuracy are removed so that the model works faster and more efficiently.<\/p>\n
One advantage of model pruning is that it requires less processing power and less memory. This is particularly useful when such AI models are to be deployed on devices that are not particularly powerful, such as smartphones, sensors, or small machines in Industry 4.0. <\/p>\n
A striking example: Imagine you have a large, feature-rich torch. But if you only need light, you remove everything unnecessary \u2013 the torch becomes lighter, uses less power, and is easier to use. Likewise, in AI applications, model pruning ensures that important functions are retained, but unnecessary \u201eadd-ons\u201c are removed.<\/p>\n
Model pruning helps companies implement AI solutions cost-effectively, quickly, and with minimal resource consumption \u2013 an important step towards efficient digital transformation.<\/p>","protected":false},"excerpt":{"rendered":"