Streaming-capable ML models are primarily found in the fields of artificial intelligence, big data, smart data and automation. They are an important development for analysing large amounts of data in real time and being able to react immediately.
In contrast to classic machine learning models, which only deliver results once the complete data set is available, streaming-capable ML models work constantly with data that comes in "on the fly". This means that they process each individual piece of information as soon as it arrives. This means that decisions can be made quickly.
A typical example: In a modern factory, hundreds of sensors constantly measure temperature, humidity and machine data. A streaming-capable ML model immediately recognises when a value is unusual and sounds the alarm before any damage occurs. This prevents breakdowns and makes production more efficient.
Such models play a central role in automation and artificial intelligence, for example in real-time fraud detection in payment transactions or in intelligent transport systems. Thanks to their rapid evaluation, streaming-capable ML models ensure that companies and systems can work even better, more flexibly and more securely.
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