A model in production is a model in decay. Customer behavior shifts, upstream schemas change, and last month's 94% accuracy quietly becomes this month's 78%. We catch the degradation before your users do. Or your revenue.
The data your model was trained on described the world as it was. The world has moved. Customer segments shift. Seasonal patterns evolve. Upstream systems change their schemas or alter value distributions without notifying anyone. The gap between what the model learned and what the production data looks like widens. By the time someone notices, you have been making bad decisions on bad predictions for weeks.
We build monitoring that watches models continuously across four dimensions: data quality (feature distributions, null rates, schema conformance), prediction drift (output shifts, confidence changes), performance metrics (accuracy, latency, throughput, cost per inference), and business impact (do the predictions correlate with outcomes?). Alerts are tiered by severity and routed to the right owner. Data quality issues go to the data team. Performance degradation goes to ML engineering. Business impact anomalies go to the product owner.
Monitoring without action is observation.
We build the retraining pipelines that activate when performance degrades past defined thresholds: automated data collection from the latest production window, model retraining with hyperparameter search, evaluation against the current model on a held-out test set, and staged canary deployment with automatic rollback if the new model underperforms. Every retraining decision is tracked and auditable.
We have built monitoring for inference pipelines processing millions of daily predictions where a 2% accuracy drop translates to six-figure business impact. The monitoring caught the drift and triggered retraining within hours. Not weeks.
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