Case study
Release Ops
Promotion, rollout, and alerts for an AI service already in use.
Service watch
MAE 0.18
Drift ok
Daily
Latency, errors, and the live release
- 1
- Promoted digest
- CI
- Release path
- 24/7
- Service watch
The artifact is not the release
Release Ops is a placeholder MLOps case study about operating a service. The model is treated as a versioned input. The work is promotion, rollout, and monitoring.
Engineering approach
CI is the only way a digest reaches production. The registry stores who published it. Alerts fire on the service, not on a training loss.
- Immutable container digest
- Staged rollout before full traffic
- Dashboard tied to the release id
Results
Sample rollouts kept a previous digest ready so a bad release could be reversed from the pipeline. Replace this with your own release history.
Technical implementation
CI publishes the container, a registry records the digest, and a deployer rolls it out. Metrics and logs feed one dashboard with a rollback hook.
- GitHub Actions
- Docker
- MLflow
- Kubernetes
- Prometheus
Key features
- One digest promoted from staging to production
- Registry record for every release
- Latency, error, and saturation alerts
- Rollback wired to the same pipeline
Written by
Anuoluwa Olutayo
AI Engineer