This article details the construction of a drift detection pipeline using OpenShift AI, a platform designed for MLOps. The process involves leveraging Kubernetes and KServe to deploy a model and then automating the detection of data drift, specifically concept drift, to ensure model performance over time. AI
IMPACT Provides a practical guide for implementing MLOps pipelines to monitor and maintain model performance in production environments.
RANK_REASON Article describes the implementation of an MLOps toolchain for drift detection, not a new model release or core research.
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