ML model monitoring for teams that ship to production.
ModelWatch combines drift detection, performance monitoring, bias tracking and retraining automation in one platform, so the people who own a model see when it needs attention.
From prediction to action
- 01
Model / endpoint
- 02
SDK or batch upload
- 03
Baseline + monitors
- 04
Alert routing
- 05
Retraining pipeline
Predictions and features flow in through the SDK or batch upload. Monitors compare them to a baseline, alerts go to your on-call channels, and thresholds can optionally start a retraining run.
Data & model drift detection
Statistical tests (PSI, KS, chi-squared) and ML-based detectors compare live feature and prediction distributions against a baseline, flagging feature drift, prediction drift and concept drift.
Why it matters: Input data changes long before accuracy metrics catch up, especially when labels arrive days later. Drift is often the earliest signal you get.
- Detect when input distributions shift after a product change
- Spot seasonal drift patterns that need a model update
- Watch shared feature stores for upstream data quality issues
PSI vs. training baseline · threshold 0.20
Performance monitoring
Track accuracy, precision, recall, F1, AUC and custom metrics across model versions, data segments and time windows. Set thresholds and get alerted when a metric degrades.
Why it matters: An aggregate metric can look healthy while one segment collapses. Slicing by version and segment shows where the problem actually is.
- Compare A/B model versions in production
- Investigate a performance drop by customer segment
- Track model health across an entire portfolio
precision by segment, 7-day window
Bias & fairness tracking
Monitor predictions for demographic bias and fairness violations across protected attributes, with configurable thresholds for disparate impact, equalized odds and demographic parity.
Why it matters: A model that was fair at launch can drift out of balance as populations change. Continuous checks keep fairness reviews from being a one-time event.
- Keep lending models within fairness thresholds over time
- Monitor hiring models for demographic bias
- Track diagnostic model performance across patient populations
Retraining triggers
When drift or degradation exceeds the thresholds you define, ModelWatch can trigger your retraining pipeline automatically and records why it ran.
Why it matters: Closing the loop between monitoring and retraining removes the manual hand-off, and the audit trail explains every model refresh after the fact.
- Kick off a pipeline run when PSI crosses 0.2 on a key feature
- Require two consecutive breached windows before retraining
- Review the history of triggers for a model version
- 09:42PSI 0.31 on merchant_category
- 09:572nd consecutive window breached
- 09:58Pipeline #418 triggered
- 11:20v3.3 candidate ready for review
LLM monitoring
The same platform covers LLM-backed features: token-level drift detection, response quality scoring, hallucination detection and cost tracking. Classification, regression, ranking, recommendation, NLP and computer vision models are supported too.
Works with your ML platform and on-call tools
SDKs for Python, Java and Go. Log predictions via SDK or batch upload.
MLflow
MLOps
SageMaker
ML platform
Vertex AI
ML platform
Databricks
Data platform
Kubeflow
MLOps
PagerDuty
Alerting
Slack
Communication
Grafana
Observability
ML engineers
Monitor drift and performance in production with real-time alerts and automated retraining triggers.
Data science teams
Track model health across your portfolio, investigate degradation and keep quality up as you scale.
ML platform teams
Offer monitoring as a service to internal teams with centralized dashboards and standardized alerting.
Ready to monitor your models?
Start with three models on the free Starter plan.