Implementing Drift Detection in Production Analytics
In the world of data analytics, models do not remain static. Once deployed in production, they interact with evolving data that may not resemble the training environment. This leads to a common challenge known as data drift—a phenomenon where the statistical properties of data change over time, reducing model accuracy and reliability. For organizations relying on analytics to power decisions, drift detection is not just a technical exercise; it’s a safeguard for business outcomes.