The MLOps Production Playbook

MLOps is the discipline of deploying and maintaining machine learning models in production reliably and efficiently. In this playbook, we share the practices that have worked across dozens of production deployments.

Version everything: data, code, models, and configurations. Reproducibility is non-negotiable. We use a combination of DVC for data versioning, Git for code, and a model registry for trained artifacts.

Automate testing beyond unit tests. Model validation should include data validation, training pipeline tests, model quality gates, and integration tests against downstream services. Our CI pipeline runs all of these before any model is promoted to production.

Monitor proactively. Don't wait for user complaints to discover model degradation. Track prediction distributions, feature drift, latency percentiles, and business metrics. Set up alerts with clear runbooks so on-call engineers can respond quickly.