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GANTRY
Make every model deployment observable and trustworthy
ABOUT
Teams deploying ML models to production face a blind spot — once a model is live, knowing whether it's performing correctly requires manual spot checks and user surveys. Data drift, corrupted inference pipelines, and stale training data can degrade model quality for days before anyone notices. Gantry continuously monitors inference pipelines, detects data distribution shifts in real time, and alerts teams when model inputs fall outside the training distribution. It also runs data quality checks on incoming features and surfaces actionable recommendations for retraining schedules.
INSTALL
pip install gantryINTEGRATION GUIDE
1. Detect data drift in production ML pipelines in real time and alert teams before model quality degrades
2. Monitor input data quality with automated schema validation and distribution checks on every inference batch
3. Track model prediction distributions against training baselines to identify concept drift early
4. Create automated retraining triggers based on drift severity, data freshness, and prediction confidence
5. Build production dashboards that show model health, data quality metrics, and drift trends at a glance
TAGS
monitoringobservabilitymlopsdrift-detectiondata-qualitypythonmodel-monitoringautomation