The term \"Inline ML Monitoring\" belongs in the categories Artificial Intelligence, Big Data and Smart Data, as well as Industry and Industry 4.0. It describes a method used by companies to monitor their deployed machine learning (ML) models during ongoing operations.<\/p>\n
Imagine that in car production, an artificial intelligence continuously checks the quality of components. Inline ML monitoring now ensures that the AI also remains reliable: It continuously monitors the data and results that the AI model delivers in the real production process. This allows companies to detect early on when the artificial intelligence makes errors, for example, because the data has changed or the model has \u201eunlearned\u201c.<\/p>\n
The great advantage of inline ML monitoring: problems are detected directly before they have a major impact on product quality or operations \u2013 and this saves time and costs. This approach is particularly important in data-driven areas, such as industry, in order to achieve consistently good results and ensure the reliability of AI solutions.<\/p>","protected":false},"excerpt":{"rendered":"