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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.