How Uber manages anomalies in its machine learning models

AI models are as good as the data they are fed with. But what if the data that’s being fed is no more relevant? What kind of results can you expect when there’s not a single anomaly in the data set but the entire data set turns anomalous? That’s exactly what data scientists have been struggling with since the onset of Covid-19. Uber was faced with a similar problem last year with hundreds of terabytes of its historical data suddenly adding no value in anomaly detection. Uber then decided to adopt recency bias in its ML models.

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