A machine-learning system can keep running while its answers quietly get worse. Monitoring catches this silent failure by observing data, resources, behavior, and outputs, so teams can adjust or retrain the model before degradation becomes expensive. section 3.4.6 section 6.2.5
A practical monitoring system combines conventional service telemetry with ML-specific checks:
- Track resource metrics such as CPU, memory, GPU utilization, and GPU temperature. section 6.2.5
- Track performance and service health through latency, queries per second, response codes, and error rates. section 3.4.6 section 6.2.5
- Detect data drift through missing values, uniqueness, standard-deviation shifts, and text-quality measures such as readability, complexity, toxicity, and jailbreak similarity. section 3.4.6 section 6.2.5
- Segment observations by region or experiment when aggregate metrics could hide localized failures. section 3.4.6
Use both system monitoring and ML monitoring. Prometheus, Grafana, or the ELK stack can cover familiar service and resource metrics, while whylogs and LangKit help summarize and evaluate changing model inputs and outputs. Monitoring can begin in training and data pipelines, not only after deployment, improving data governance and compliance. section 6.2.5 section 3.4.6
Rolling text profiles
A whylogs logger configured with LangKit can consume text continuously, write local profiles every five minutes, and expose summarized metrics for later drift analysis. A Kubernetes sidecar can move logging out of the application’s critical path by collecting requests or standard output and forwarding processed records to a monitoring backend.
