What is Data Drift

Definition

Data Drift is a change in the distribution of input data after running a model. Simply put, this concept helps train models, compare approaches, and reduce the risk of errors on new data. In practice, it helps to understand what capabilities the tool actually has, what data it will need, and what limitations are worth checking before implementation.

Example

Users from another country began to come to the service, and the features no longer resemble the data on which the model was trained.

Why it matters

Data drift helps to notice in time that the quality of the model may deteriorate without changing the code. This helps you choose AI tools not by big promises, but by how they work in a real problem.

How it works

First, the problem is translated into data and metrics, then the model is trained, tested on a separate sample, and compared with alternatives. In the case of the term “Data Drift”, it is important to look separately at the data, quality criteria and application conditions.

Where it is used

• Used in training, testing and tuning models, in automatic selection of parameters, forecasting, classification and recommendation systems.

Limitations

The main limitation is the dependence on data, metrics and verification conditions. A good result on a test does not always mean reliable performance in a real product.