An MLOps Readiness Checklist

Ten questions

Are runs tracked with code, data and environment versions? Can the model be reproduced? Is preprocessing packaged with it? Are inputs validated in training and serving? Are features point-in-time correct and shared between training and serving? Is the model in a registry, served by alias, with gates against the champion? Is rollout gradual with shadow or canary? Are service, data and model signals monitored with alerts? Is there a retraining plan and a rollback runbook? Is there a model card with an owner?

The checklist

Use it at launch reviews.

[ ] tracked runs: params, metrics, git commit, data fingerprint, env
[ ] reproducible training; seeds and versions pinned
[ ] packaged pipeline + manifest + checksum
[ ] data validation in training and serving
[ ] point-in-time features; shared feature code (no skew)
[ ] registry + alias-based serving + evaluation gate (with slices)
[ ] shadow and/or canary rollout with automatic rollback
[ ] monitoring: service, data drift, predictions, delayed labels
[ ] retraining triggers + cadence backed by evidence
[ ] model card, owner, access control, incident runbook

Start with tracking and monitoring

If you can only do two things this month, track every run and monitor every live model.

त्वरित जाँच: Which item belongs on an MLOps readiness checklist?

• Recomputing scaling statistics on each request
• Emailing model files to the ops team
• Alias-based serving from a registry with an evaluation gate
• Skipping monitoring for small models