Machine learning models aren’t static—they degrade over time. This phenomenon, known as model drift, happens when the data a model sees in production begins to diverge from what it was trained on. Left unchecked, this can lead to bad decisions and faulty predictions.

ADAT tackles this with a built-in mechanism for drift detection and remediation:

• It uses the Kolmogorov-Smirnov test to monitor statistical changes in prediction distributions.

• When drift is detected, the system initiates retraining using updated production data.

• This process is logged and cached for transparency, enabling reproducibility and auditability.

For example, a fraud detection model might see changes in transaction patterns over time. ADAT would detect these changes and adjust the model, ensuring its predictions remain accurate.

This makes ADAT not just a platform for building models, but for maintaining them—an often-overlooked aspect of production-grade analytics. For teams with limited resources, this automated monitoring and correction can significantly reduce technical debt and ensure long-term performance.