Monitoring (ML Context)
Definition
ML monitoring is the continuous observation of model performance, data quality, and system health in production, enabling detection of degradation, drift, and anomalies before they impact business outcomes.
Why It Matters
Traditional software either works or it doesn’t, and monitoring catches crashes and errors. ML systems fail silently. The model keeps returning predictions, just increasingly wrong ones. Without specialized monitoring, you won’t know until users complain or business metrics tank.
ML monitoring spans multiple layers: infrastructure (is the service up?), performance (is it fast?), model quality (is it accurate?), and data quality (is input valid?). Missing any layer leaves blind spots that will eventually hurt you.
For AI engineers, LLM monitoring has unique challenges. Output quality is subjective. “Good” responses are hard to measure automatically. Token costs accumulate silently. Prompt injection attacks need detection. Traditional monitoring doesn’t cover these.
Implementation Basics
Comprehensive ML monitoring covers four domains:
1. Operational Monitoring Standard infrastructure metrics: latency (p50, p95, p99), throughput (requests/second), error rates, GPU utilization, memory usage, queue depths. These catch infrastructure problems that affect model availability.
2. Data Quality Monitoring Input validation: schema compliance, missing values, out-of-range values. Distribution monitoring: feature statistics compared to baseline. Data drift detection: statistical tests for distribution shift. Catch bad data before it causes bad predictions.
3. Model Performance Monitoring When you have labels: accuracy, precision, recall, F1, NDCG. When you don’t: prediction distribution, confidence calibration, output consistency. Monitor cohorts separately because aggregate metrics hide segment-specific problems.
4. Business Outcome Monitoring Connect model predictions to business results: click-through rates, conversion rates, user satisfaction, support tickets. These are the metrics that actually matter, while technical metrics are proxies.
For LLM Applications:
- Token usage and costs per request/user
- Latency breakdown (time-to-first-token, total generation time)
- Response quality scores (if you have evaluation pipelines)
- Safety violations (prompt injection attempts, harmful output detection)
- User feedback signals (thumbs up/down, regenerations, abandonment)
Tools: Prometheus + Grafana for infrastructure, specialized platforms (Arize, WhyLabs, Evidently) for ML-specific monitoring, custom dashboards for business metrics.
Start monitoring before you launch. The best time to establish baselines is when things are working correctly.
Source
ML monitoring tracks prediction performance, resource usage, and data quality to ensure models perform as expected in production environments.
https://cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning