Keeping a constant eye on an AI system after it goes live — watching for declining performance, unexpected behaviour, or new risks as data and conditions change.
Keeping a constant eye on an AI system after it goes live — watching for declining performance, unexpected behaviour, or new risks as data and conditions change.
Continuous monitoring in AI extends traditional application monitoring (availability, latency, error rates) with AI-specific metrics: prediction distribution monitoring (detecting data drift and concept drift), performance monitoring (tracking accuracy, fairness, and calibration against ground-truth labels), data quality monitoring (input feature distribution checks), and behavioural monitoring (detecting anomalous output patterns). The EU AI Act's post-market monitoring requirements under Article 72 mandate continuous monitoring for high-risk AI systems. MLOps platforms and feature stores increasingly provide automated drift detection and alerting as platform capabilities.
Investing in MLOps monitoring infrastructure pays for itself by catching model degradation before business impact — organisations without continuous monitoring discover problems only through customer complaints or regulatory audits.
Like continuous structural health monitoring for a bridge — sensors measure strain, vibration, and temperature continuously so that developing problems are detected and addressed before they become failures.
Keeping a constant eye on an AI system after it goes live — watching for declining performance, unexpected behaviour, or new risks as data and conditions change.
Continuous monitoring in AI extends traditional application monitoring (availability, latency, error rates) with AI-specific metrics: prediction distribution monitoring (detecting data drift and concept drift), performance monitoring (tracking accuracy, fairness, and calibration against ground-truth labels), data quality monitoring (input feature distribution checks), and behavioural monitoring (detecting anomalous output patterns). The EU AI Act's post-market monitoring requirements under Article 72 mandate continuous monitoring for high-risk AI systems. MLOps platforms and feature stores increasingly provide automated drift detection and alerting as platform capabilities.
Investing in MLOps monitoring infrastructure pays for itself by catching model degradation before business impact — organisations without continuous monitoring discover problems only through customer complaints or regulatory audits.