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The helpful part comes from connecting these items. A trained model is just one a part of a production ML system. When the speed crosses that threshold, we don’t immediately assume that the mannequin has failed. Don’t wash the car with high strain. The sensor readings in winter don’t observe the same patterns because the summer season readings. In our instance, the change in sensor behaviour is linked to the distinction between the summer knowledge used for training and the winter data being processed later. Later, we launched winter data through the inference stage. We used historic pump information and replaying it to simulate a dwell stream. For an industrial use case like water pump anomaly detection, the encompassing process issues just as a lot. A sustained increase within the anomaly price is way more helpful. A single anomalous prediction doesn’t inform us a lot. "Something doesn’t feel right" is the best way they can describe it. The API can nonetheless be healthy.