Model release engineering

From a trained artifact to a version in production: registry and versioning, evaluation gates, CI/CD for ML, GitOps and Terraform, staged rollouts.

Model release engineering covers the path from a trained artifact to a version serving production traffic: how the model is packaged and versioned, what evidence it must produce before promotion, and how automation moves it through environments. The lessons go from registry and evaluation gates through CI/CD and GitOps to progressive rollout and rollback.

Lessons (5)

• Model registry, packaging and versioning · Practice

  A model registry is the system of record between training and serving: it gives each trained artifact a name, an immutable version, lineage back to the run that produced it, and a state that says whether it may be deployed.

• Model evaluation: regression suites and release gates · Practice

  An evaluation gate turns the question of whether a model is good enough into an automated, repeatable check that blocks promotion when it fails.

• CI/CD for models and services with GitHub Actions · Practice

  CI/CD for machine learning extends ordinary software delivery with two extra artifacts, data and models, and a third loop, continuous training.

• Reproducible environments: GitOps with Argo CD and infrastructure as code with Terraform · Practice

  Reproducible environments come from declaring everything in version control and letting automation converge real systems toward it.

• Staged rollouts: shadow, canary, champion/challenger and rollback · Practice

  Offline evaluation cannot prove a model behaves well on live traffic, so releases are staged: the new version is exposed to production conditions gradually while automated checks decide whether to continue.

Changes

• Version 1, 2026-10-14: re-cut from the role pathway groups into a topic module (site 2026.10.2); no lesson changed