Different approaches are used to put machine learning models in production. Quite often models are put into production after one-off training (stationary models). For such a model to keep predicting accurately over time, the data that it is making predictions on must have a similar distribution as the data on which the model was trained. However, distributions can drift over time.

Such deviations are termed concept drift.

(A concept in “concept drift” refers to the unknown and hidden relationship between inputs and output variables. Concepts are also known as covariates.)

Figure 1: Types of concept drift — Sudden, Gradual, Incremental and Reoccuring
Figure 1: Types of concept drift — Sudden, Gradual, Incremental and Reoccuring
Figure 1: Types of concept drift

Techniques that tackle variation in concept over time can be…

Aman Singhal

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