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Production MLOps: Automated Concept Drift Detection and Retraining Loops
How to detect model decay in live production streams using Kolmogorov-Smirnov statistical tests and containerized Kubeflow pipelines.
Preventing Machine Learning Decay in Production
A machine learning model is only as good as the similarity between training data and live production inference streams.
When market conditions, user demographics, or macro trends shift, models experience Concept Drift and Data Drift.
Automated Drift Pipeline Architecture
- Continuous telemetry logging of inference inputs and predictions.
- Daily Statistical KS-tests against training distribution baselines.
- Automated trigger of containerized Airflow / Kubeflow retraining jobs when p-value < 0.05.