The term interoperability of ML models finds its place primarily in the fields of Artificial Intelligence, Big Data and Smart Data, as well as Digital Transformation. It describes the ability of different machine learning models (ML models) to work together seamlessly and exchange data, even if they come from different platforms or manufacturers.
In everyday practical terms, this means: 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 entirely different systems – for instance, one might run on a cloud platform, and another directly on company servers. Interoperability ensures that the results and functions of these different models can be easily combined without having to re-program everything from scratch.
A vivid example: An online shop uses one ML model for product recommendations and another for fraud detection. Thanks to ML model interoperability, these two systems can exchange their data and results to make secure and relevant suggestions to the user. This increases efficiency and flexibility for businesses and enables innovation through the interplay of various AI applications.
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