According to the US Census Bureau’s survey of 583,000 US companies in 2018, only 2.8% uses machine learning to leverage advantages to their operations. About 8.9% of surveyed use some form of AI such as voice recognition.
You spent weeks if not months training a machine learning model, and finally, it’s moved to production. Now, you should be seeing the benefits of your hard work.
But instead, you notice that model performance is slowly degrading over time. What could cause this?
If not monitored constantly and adequately evaluated for predictive quality degradation, concept drift can kill a machine learning model before its expected retirement date.
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