Model monitoring is an important term in the fields of artificial intelligence, big data and smart data, as well as automation. It describes the regular monitoring of models that, for example, make predictions or automate processes. This monitoring is crucial to ensure that AI models continue to work reliably and accurately after they have been launched.<\/p>\n
Imagine you're using an AI model that predicts your product's sales for the next month. Initially, the model delivers very good results. However, over time, customer behaviour or market conditions change. Without model monitoring, inaccurate predictions would go unnoticed \u2013 and incorrect business decisions could be the result.<\/p>\n
Model monitoring helps to recognise such changes early on. It continuously monitors the model's performance, reports deviations and provides indications when adjustments are necessary. This enables companies to secure the quality of their automation and data-driven decisions in the long term and react flexibly to changes in the market. <\/p>\n
Model monitoring is therefore an important building block, especially for data-driven applications, for extracting the maximum benefit from Artificial Intelligence and Big Data.<\/p>","protected":false},"excerpt":{"rendered":"