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Is my model ready for production?

Fourteen checks from packaging to rollback. Tick them off; your progress stays in your browser.

Production is where a model meets data it was not trained on, at a scale nobody tested by hand. Most incidents trace back to a missing item below.

Background Small, Reversible Bets · Incident Response for ML

0 of 14 checked

Build The model is packaged with its exact environment, and the artifact is versioned.Model Packaging & Containers Training can be reproduced: pinned dependencies, seeds and a versioned dataset.Reproducibility · Experiment Tracking It is registered with its lineage — which data, code and parameters produced it.Model Registry & Versioning · Lineage Tracking

Test Input data pass quality gates (schema, ranges, missing values, freshness).Data Quality Gates The release gate checks metrics per segment, not only overall.CI/CD for ML Performance has been compared across relevant groups for fairness.Fairness Audits Latency is load-tested at the 99th percentile, at expected peak traffic.Latency & Throughput · Auto-Scaling Endpoints

Release It runs in shadow mode or as a canary first, with enough traffic to detect a regression.Shadow Mode & Champion/Challenger · A/B & Canary Rollouts Rolling back is a single, tested step.Model Registry & Versioning · Incident Response for ML

Operate Inputs, predictions and — when they arrive — outcomes are monitored.Model Monitoring Dashboards · Drift Detection in Production There are SLOs with burn-rate alerts, and an owner who gets paged.Alerting & SLOs A fallback answers when the model cannot (rules, cache, previous model).Incident Response for ML · Caching & Prediction Stores The cost per prediction is known and tracked.Cost Governance Retraining has a trigger, a schedule and a validation gate.ML Pipelines · Orchestration & Scheduling

Last reviewed: 2 October 2026 · How this site checks its content · Report a mistake