Performance Monitoring is the continuous collection, analysis, and visualisation of metrics describing how a system, application, or model behaves under real workloads. It tracks indicators such as latency, throughput, error rates, resource utilisation, and, for machine-learning systems, predictive quality and drift, surfacing degradation through dashboards and alerts. Performance monitoring is a core observability discipline that enables teams to detect regressions, diagnose bottlenecks, and uphold service-level objectives.
Overview
- Performance Monitoring turns raw Telemetry into actionable insight about system health. Instrumentation emits metrics, traces, and logs that are aggregated, evaluated against thresholds, and rendered on dashboards, with Anomaly Detection surfacing deviations automatically. For machine-learning systems it extends beyond infrastructure to model accuracy, data drift, and prediction latency, making it inseparable from MLOps and AI Monitoring practice.
Key aspects
- Continuous collection of latency, throughput, and error metrics.
- Dashboards and alerting against service-level objectives.
- Anomaly Detection to surface regressions automatically.
- Model-quality and drift tracking for ML workloads.
- Closing the Feedback Loop into operations and remediation.
Applications
- Application performance management for web services.
- Model drift and accuracy tracking in MLOps pipelines.
- Capacity planning and bottleneck diagnosis.
- Reliability engineering and incident response.