MLOps Production Checklist¶

Introduction¶

This checklist is for engineers reviewing whether a model is ready to operate, not just whether it scored well in a notebook.

Why This Matters¶

Production readiness reduces incident risk. It also makes reviews easier because every model version has evidence: data, code, metrics, artifact, deployment, monitoring, and rollback.

Core Concepts¶

The checklist covers reproducibility, data validation, model evaluation, registry metadata, deployment, monitoring, rollback, security, and documentation.

Practical Example¶

A release checklist can live in version control:

release: churn-api-2026-05-30
model_version: churn:17
data_version: customers-2026-05-30
image: registry.example.com/churn-api:9f3a21c
checks:
  data_validation: passed
  unit_tests: passed
  f1_threshold: passed
  staging_smoke_test: passed
  rollback_version: churn:16

How This Fits in a Production Workflow¶

Use the checklist at promotion time and during incident review. If a check cannot be answered, the production process has a gap.

Common Mistakes¶

• Treating the checklist as paperwork after release.
• Not assigning an owner.
• Missing rollback instructions.
• Monitoring infrastructure but not model behavior.

Quick Checklist¶

• Can training be reproduced?
• Are metrics and data versions recorded?
• Is the model in a registry?
• Is deployment automated?
• Are logs, metrics, and drift checks active?
• Is rollback tested?
• Is a runbook available?

Related Guides¶

• What Is MLOps? A Practical Guide for Beginners
• CI/CD Pipeline for Machine Learning Projects
• Model Monitoring in Production

Summary¶

Use this practical MLOps checklist before deploying or operating a machine learning model in production.