A growing number of decisions and critical business processes rely on models produced with machine learning and other statistical techniques.
For a variety of reasons, the inputs and outputs from these models can “drift” over time, and produce unexpected behavior and a decrease in predictive accuracy. Unfortunately, this drift often goes unrecognized because of inadequate tools or internal processes, leading to severe financial loss or a degraded customer experience.
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This paper represents best practices Domino has learned from 5+ years of working with data science leaders at companies such as Allstate, Bayer, Dell and Moody’s Analytics.