Monitoring built for how ML models actually fail.
ModelWatch monitors production ML models for drift, performance degradation, bias and anomalies, and alerts teams before model failures affect business outcomes.
A monitoring layer between your models and your on-call team.
You log predictions and features from any model — classification, regression, ranking, recommendation, NLP, computer vision or LLM. ModelWatch compares them against a baseline, tracks performance as labels arrive and checks fairness metrics continuously.
When something moves past a threshold, it tells the right channel and, if you choose, starts retraining.
Infrastructure monitoring can't see a model going wrong.
- Latency and error rates stay green while inputs drift away from training data.
- Ground-truth labels often arrive days later, so accuracy alone is a lagging signal.
- A model can stay accurate overall while one segment or group degrades.
- Retraining tends to happen on a calendar, not when the data says it's needed.
Drift detection
Feature, prediction and concept drift using PSI, KS, chi-squared and ML-based detectors.
Performance monitoring
Accuracy, precision, recall, F1, AUC and custom metrics by version, segment and window.
Bias & fairness
Disparate impact, equalized odds and demographic parity checked against your thresholds.
Retraining triggers
Threshold-based pipeline triggers with a full audit trail.
- step 1
Log
Send predictions and features via the Python, Java or Go SDK, or batch upload.
- step 2
Baseline
Pick a training set or production window as the reference.
- step 3
Monitor
Run drift, performance and fairness checks with per-model thresholds.
- step 4
Respond
Alert via email, Slack or PagerDuty and optionally trigger retraining.
ML engineers
Real-time drift and performance alerts on the models they deploy.
Data science teams
Portfolio-wide model health and tools to investigate degradation.
ML platform teams
Centralized dashboards and standard alerting offered to internal teams.
Explain every alert
An alert names the model, version, feature or segment, the test that fired and the threshold it crossed.
Calibrate, don't spam
Thresholds and windows are set per model, so noisy features don't drown out the ones that matter.
Humans stay in the loop
Automated retraining is opt-in and every trigger is recorded, so teams can review why a model changed.
Your data stays yours
You keep ownership of predictions, features and configurations; they are processed only to provide monitoring.
See it on your own model.
The Starter plan is free for up to three models.