datadriftsovervågning

datadriftsovervågning refers to the process of monitoring changes in the characteristics of data over time. This phenomenon is particularly relevant in machine learning and data science, where models are trained on historical data and then deployed to make predictions on new, unseen data. If the underlying distribution of the new data deviates significantly from the data the model was trained on, the model's performance can degrade, a problem known as model drift or concept drift.

The primary goal of datadriftsovervågning is to detect these deviations early. This allows for timely intervention, such as retraining the model with updated data, adjusting model parameters, or even switching to a different model altogether. Without effective datadriftsovervågning, deployed models can become inaccurate and unreliable, leading to poor decision-making and potentially negative consequences.

Common approaches to datadriftsovervågning involve comparing statistical properties of the incoming data to a baseline or reference dataset. Metrics such as mean, variance, and distribution shape can be monitored. More sophisticated techniques employ statistical tests or machine learning models specifically designed to detect distribution shifts. The frequency of monitoring depends on the application and the expected rate of data change. Implementing robust datadriftsovervågning is a crucial aspect of maintaining the accuracy and effectiveness of data-driven systems.