DriftGuard: Detecting Silent Data Drift in Production ML Pipelines

Abstract

Production machine-learning systems fail quietly: input distributions drift, model accuracy degrades, and the first signal is often a downstream business metric weeks later. We introduce DriftGuard, a monitoring method that detects feature- and prediction-level drift using a streaming two-sample test with controlled false-alarm rate. DriftGuard requires no labels at inference time and runs in-line with serving at negligible overhead. Across five production datasets from financial and logistics domains, DriftGuard detects injected drift a median of 11 days earlier than accuracy-based monitoring, with a 3x lower false-alarm rate than fixed-threshold baselines. We provide an open implementation and integration guidance for MLOps pipelines.

The Silent Failure

Deployed models degrade as input distributions shift, but the first visible signal is usually a lagging business metric. By then, weeks of poor decisions have accrued.

Method

DriftGuard runs a streaming two-sample test over features and predictions with a controlled false-alarm rate. It needs no inference-time labels and adds negligible serving overhead.

Evaluation

Across five production datasets in finance and logistics, DriftGuard flags injected drift a median of 11 days earlier than accuracy-based monitoring, with a 3x lower false-alarm rate than fixed-threshold baselines.

Cite

@article{scalejade2026driftguard,
  title  = {DriftGuard: Detecting Silent Data Drift in Production ML Pipelines},
  author = {ScaleJade Research and Sari, N. and Lim, K.},
  year   = {2026},
  note   = {Preprint},
  url    = {https://www.scalejade.com/research/driftguard}
}