A computational advertising model trained on historical data may quickly become obsolete if it is not updated frequently to account for nonstationary distribution changes, such as failing to incorporate the launch of a new, popular device.

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A nonstationary distribution occurs when the underlying probability distribution of the data changes slowly over time. In machine learning systems, if the deployment distribution drifts steadily and the model is not updated adequately to reflect these changes, its predictive performance will gradually deteriorate.

Nonstationary Distribution

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Computational Advertising Example of Nonstationary Distribution

A spam filter may initially work well at detecting all known spam, but its performance degrades over time due to a nonstationary distribution as spammers adapt and craft new, unfamiliar message formats to evade detection.

Spam Filter Example of Nonstationary Distribution

A product recommendation system exemplifies the challenge of nonstationary distributions if its underlying model does not adapt to seasonal shifts, such as continuing to recommend winter holiday items long after the season has passed.

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