The term inline ML monitoring is at home in the categories of artificial intelligence, big data and smart data as well as industry and Factory 4.0. It describes a method that companies use to monitor their machine learning models (ML models) during operation.
Imagine an artificial intelligence constantly checking the quality of components in car production. Inline ML monitoring now ensures that the AI remains reliable: It continuously monitors the data and results that the AI model delivers in the real production process. This allows companies to recognise at an early stage if the artificial intelligence makes errors, for example because the data has changed or the model has "unlearned" itself.
The major advantage of inline ML monitoring is that problems are recognised immediately before they have a major impact on product quality or operations - and that saves time and money. This approach is particularly important in data-driven areas such as industry in order to continuously achieve good results and ensure the reliability of AI solutions.
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