Targeted model adaptation is particularly relevant in the fields of artificial intelligence, big data and smart data, and digital transformation. This involves modifying digital models \u2013 such as forecasting models or software models \u2013 so that they fulfil a specific purpose even better. <\/p>\n
Imagine a company using software that predicts how much of a product will be sold in the coming months. If customer purchasing behaviour suddenly changes, for example due to a price promotion, the old model might provide inaccurate results. With targeted model adaptation, the model is then specifically adjusted to the new conditions \u2013 for instance, by using new data or altering certain calculations. This keeps the prediction useful and reliable.<\/p>\n
The advantage of targeted model adaptation: Models are kept up-to-date quickly and effectively, without having to start from scratch. This saves time and costs. With this method, companies remain flexible and competitive because they can react quickly to changes.<\/p>","protected":false},"excerpt":{"rendered":"