The term interoperability of ML models is primarily used in the fields of artificial intelligence, big data, smart data and digital transformation. It describes the ability of different machine learning models (ML models) to work together and exchange data without any problems, even if they are based on different platforms or come from different manufacturers.
In practice, this means that companies often use ML models to perform tasks such as image recognition, fraud prevention or predicting customer behaviour. However, these models are often developed in completely different systems - for example, one runs on a cloud platform, the other directly on company servers. Interoperability ensures that the results and functions of these different models can be easily combined without having to reprogramme everything.
An illustrative example: an online shop uses one ML model for product recommendations and another for fraud detection. Thanks to the interoperability of ML models, these two systems can exchange their data and results in order to make safe and suitable suggestions to the user. This increases efficiency and flexibility for businesses and enables innovation through the interaction of different AI applications.
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