ML Model Monitoring
Models degrade silently. Input distributions shift, upstream data changes, and prediction quality drops — but nobody notices until customers complain. We deploy monitoring that catches model degradation early with data drift detection, prediction tracking, and automated alerts.
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Data Drift Detection
We deploy statistical drift detection on model inputs — PSI (Population Stability Index) and KS tests compare incoming feature distributions against training baselines. Drift scores get computed on sliding windows (hourly/daily) depending on traffic volume. When drift exceeds thresholds, alerts fire and the monitoring dashboard highlights which specific features shifted. Tools: Evidently AI, NannyML, or custom Prometheus-based checks.
Prediction Quality Tracking
For supervised models with delayed ground truth, we track proxy metrics — prediction confidence distributions, output value ranges, and prediction class balance. When ground truth labels arrive, we compute actual accuracy, precision, recall, and AUC over time windows. Quality metrics get compared against baseline thresholds set during model validation.
Operational Monitoring
Beyond model quality: request latency, error rates, throughput, GPU utilization, and memory usage per model endpoint. Prometheus scrapes metrics from serving infrastructure. Alertmanager routes issues — latency spikes go to the infra team, accuracy drops go to the ML team. Everything correlates in a single Grafana dashboard.
Automated Remediation
Critical drift triggers automated responses: retraining pipeline kickoff, traffic rerouting to a fallback model, or autoscaling if the issue is load-related. Runbooks document escalation paths for issues automation can't handle. You get monitoring that doesn't just detect problems — it starts fixing them before your team wakes up.
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