A detailed analysis of a production AIOps system reveals a common but painful problem: model accuracy plummeting from 92% to 67% within three months of deployment. The post identifies three primary causes: data distribution shifts due to changing operational patterns, feature decay as static features lose relevance, and inadequate retraining cycles that fail to adapt to new data. The author provides a systematic repair process including continuous monitoring of prediction confidence, automated retraining triggers based on drift detection, and feature engineering updates. This case study is essential reading for MLOps and SRE teams who deploy AI for anomaly detection or root cause analysis, as it offers practical, battle-tested solutions to maintain model performance over time. The insights are not limited to AIOps but apply broadly to any production machine learning system facing concept drift.
A deep post-mortem on AIOps model accuracy dropping from 92% to 67% in 3 months, with root causes and repair strategies.