What is the difference between data shift and model drift?

Tiara Williamson Answered

Data tremendously influences how businesses work, regardless of type, industry, or geographical point of existence. Visible changes in data (work) flow are constantly monitored by data analysts who extract outcomes as a result of that processing. Data shift refers to the undocumented and unexpected changes to data infrastructure and semantics that leads to processes breaking and corrupting the data. This is not necessarily a step back. Drifts in Machine Learning development can create new ways of data handling and pathways in the model inputs and predictions.

One of the main causes of drift in Machine Learning development comes from data sources or destinations. Unbiasing the data before incorporating it in model training can prevent the drifts during processing. One more type of drift has a great impact on Machine Learning model development – Model drift, defined as a model’s performance degradation due to changes in inputs/outputs variables and changes in data itself. It has a negative impact on business and development workflow, and can happen at any moment of model development.

This can be observed through the model’s prediction performances and outputs. Changes in real-world environments also can cause model drifts. To ensure a long-term and stable Machine Learning model, it is necessary to monitor the performance and its metrics, unbias the data and retrain the model continuously. There are specialized monitoring tools and platforms that provide monitoring systems for different types of metrics and methods available on the market, so feel free to further investigate to find the one that suits your model needs. There are 2 main types of model drift:

Data drift– occurs when input values change.
Concept drift – occurs when the relationship between target variables and input changes. It leads to changes in the models’ prediction capabilities and outcomes. Concept drifts can be recurring, sudden, or gradual.

Gradual can be determined through model unavailability to follow modern trends because it was trained on historical facts/inputs.
- Sudden changes in the market (during natural disasters or epidemics) model won’t be considered valuable, so it degrades the trustworthiness of its output.
- Recurring follows up on seasonal events. If the model does not incorporate those trends as valuable, it can’t provide upright data for those periods.