Your models degrade.
Inferpathio doesn't let them.
Version every artifact, detect drift before accuracy tanks, trigger retraining automatically — and keep your ML team shipping instead of firefighting.
Models degrade in silence
The average production ML model loses 15–40% of its effectiveness within 6 months. Most teams don't notice until users complain.
Drift goes undetected
Input distributions shift weekly. Without automated monitoring, your accuracy metrics are weeks behind reality.
Version chaos
Model weights in S3. Configs in git. Metrics in a spreadsheet. No single lineage record when you need to roll back.
Manual retraining loops
Engineers spend Friday afternoons running retrain scripts. No automation means human latency between drift and fix.
The full lifecycle, automated
Four stages, one platform. From experiment to production and back — all tracked, versioned, and automated.
Experiment
Log runs, parameters, and metrics. Compare across experiments with a unified lineage graph.
ExploreVersion
Register model artifacts with full metadata. Roll back to any version in seconds with immutable snapshots.
ExploreMonitor
Continuous drift scoring on input features and predictions. Alerts before accuracy crosses your SLA threshold.
ExploreRetrain
Triggered automatically or on schedule. New model version registered and promoted if validation passes.
ExploreFrom signal to fix in under 10 minutes
Inferpathio computes Population Stability Index and Kolmogorov-Smirnov scores continuously. When a threshold is crossed, your retraining pipeline fires automatically — no Slack message required.
Everything the model lifecycle needs
Built for ML engineers who are tired of duct-taping five tools together.
Immutable model registry
Every artifact, every parameter, every lineage edge. Content-addressed storage so rollbacks are deterministic.
Multi-metric drift scoring
PSI, KS-test, Wasserstein distance, and custom statistical tests. Configurable per feature group.
Policy-based retraining
Define trigger conditions in YAML. Drift threshold, schedule, upstream data freshness — all composable.
Experiment comparison
Side-by-side metric comparison. Filter by tag, date range, or config diff. Promote the best run directly to registry.
Framework-agnostic SDK
Three lines to integrate. Works with PyTorch, TensorFlow, scikit-learn, XGBoost, and any custom training loop.
Audit trail and governance
Immutable event log for every promotion, rollback, and retraining trigger. Role-based access controls with exportable audit logs.
Three lines. Any framework.
The Inferpathio SDK wraps your existing training code without restructuring it. Add monitoring after the fact with the same simplicity.
# Install
$ pip install inferpathio
# Add to your training script
from inferpathio import track, monitor
with track("churn-model") as run:
model.fit(X_train, y_train)
run.log_metric("auc", 0.913)
run.register()
# Monitor production traffic
monitor("churn-model").watch(X_live)The ops layer ML was missing
We had three engineers spending two days a week just tracking model states. Inferpathio reduced that to a five-minute Slack thread. Our team now ships two more model iterations per sprint.
The drift monitoring caught a feature pipeline regression we'd have missed for two weeks. Automatic retraining kicked in and accuracy was back within 20 minutes. That would have been a P1 incident otherwise.
Start monitoring your models in 15 minutes
Free plan, no credit card, no infrastructure setup. Connect your first model with three lines of Python.