Anomaly Detection in DevOps Pipelines using Deep Learning

Abstract

Modern DevOps pipelines emit a torrent of telemetry — CI/CD events, build and deploy logs, metrics, and traces — far beyond what humans or static thresholds can monitor. Anomalies in this stream (failing builds, latency spikes, error surges, configuration drift, resource exhaustion) are early signals of incidents, yet they are rare, varied, and buried in noise. This article surveys and organizes deep-learning approaches to detecting them. We frame anomaly detection as a largely unsupervised problem over high-volume, partly unstructured data, and present a four-layer detection stack: ingestion and log parsing, deep detection models, scoring and thresholding, and triage and action. We cover the three dominant model families — forecasting models (LSTM/GRU next-event prediction, exemplified by DeepLog), reconstruction models (autoencoders flagging high reconstruction error), and sequence/transformer models — and formalize their detection criteria and the precision/recall/F1 metrics used to evaluate them. Crucially, we highlight a widely documented evaluation pitfall: under a realistic chronological train/test split, reported F1 scores can collapse (for example from roughly 0.93 to below 0.5 on the BGL benchmark), a caution against over-optimistic random-split results. Public benchmark datasets (HDFS, BGL) and methods are cited throughout.