Model monitoring is an important term in the fields of artificial intelligence, big data, smart data and automation. It describes the regular monitoring of models that make predictions or automate processes, for example. This monitoring is crucial to ensure that AI models continue to work reliably and accurately even after they have been launched.
Imagine you use an AI model that predicts the sales of your products in the next month. Initially, the model delivers very good results. But over time, customer behaviour or market conditions change. Without model monitoring, incorrect predictions would go unnoticed - and wrong business decisions could be the result.
Model Monitoring helps to recognise such changes at an early stage. It continuously monitors the performance of the model, reports deviations and provides information when adjustments are necessary. In this way, companies can ensure the quality of their automation and data-based decisions in the long term and react flexibly to changes in the market.
Model monitoring is therefore an important building block for maximising the benefits of artificial intelligence and big data, especially for data-driven applications.
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