Machine Learning-Augmented DevOps: Automated Anomaly Detection and Predictive Incident Management in High-Velocity Deployment Environments
The increasing velocity of software deployments enabled by mature CI/CD practices has outpaced the capacity of human operators to detect and respond to production incidents through manual monitoring. This paper explores the integration of machine learning techniques into DevOps operational pipelines — an emerging discipline termed AIOps — with particular focus on anomaly detection and predictive incident management. We present ML-DevOps, a reference architecture that integrates unsupervised anomaly detection models (Isolation Forest, LSTM Autoencoders) with supervised incident classifiers into a continuous delivery pipeline. The architecture is evaluated using a real-world dataset comprising 14 months of telemetry from a large e-commerce platform processing over 2 million daily transactions. ML-DevOps achieves a 91.3% anomaly detection precision and a 5.2-minute mean advance warning time before customer-impacting incidents, representing an 82% improvement over threshold-based alerting baselines. We further analyze model drift in the context of continuous deployment, demonstrating that retraining frequency must scale with deployment frequency to maintain detection accuracy. This work bridges the gap between machine learning research and DevOps practice, providing both an architectural blueprint and empirical evidence for AIOps integration.
