Building an MLOps Pipeline on an Open Stack

MLOps is what you get when you treat a model like real software: versioned, tested, deployed through a pipeline, monitored in production, and instantly reversible. The goal isn't ceremony — it's confidence. You want to ship model changes as casually and safely as you ship code.

The five stages

A solid pipeline has the same shape almost everywhere:

1. Version — pin the exact data, code, and config behind a model.
2. Train — produce a candidate artifact reproducibly.
3. Evaluate & gate — block promotion unless quality, latency, and regression thresholds pass.
4. Canary deploy — send a small slice of traffic to the new model first.
5. Promote or roll back — auto-promote on success, auto-revert on regression.

An open-source stack

You can build the whole thing without proprietary lock-in:

• DVC — version datasets and pipelines alongside Git.
• MLflow — track experiments and act as the model registry.
• GitHub Actions / GitLab CI — run the pipeline on every change.
• ArgoCD + Kubernetes — GitOps deployment, canaries, and rollback.
• Prometheus + Grafana + OpenTelemetry — the observability that closes the loop.

Stage 3 is where the magic is: evaluation gates

The single most valuable part of an ML pipeline is the gate that refuses to ship a worse model. Encode your standards as code:

# .vertexstudio-ci.yaml — evaluation gates
evaluate:
  gates:
    - metric: accuracy          >  0.94
    - metric: latency_p99_ms    <  10
    - metric: regression_delta  <  0.01   # vs current prod
    - metric: safety_violations == 0
  on_fail: block_and_notify

Now "is this model better?" has a yes/no answer the pipeline enforces automatically — no human vibe-check required.

Canary deploys and zero-downtime rollout

Even a model that passes offline eval can surprise you on live traffic. A canary routes a small percentage of real requests to the new version, watches the same metrics, and only widens the rollout if they hold:

canary_deploy:
  traffic_split: 5%      # start small
  watch: [latency_p99, error_rate, cost_per_req]
  duration: 30m
  auto_promote: on_success
  auto_rollback: on_regression   # instant revert

Shadow mode goes one step further: send real traffic to the new model but don't return its answers — compare them offline. Zero user risk, full signal.

Close the loop with observability

A pipeline that deploys but doesn't watch is half a system. Token-level traces, latency heatmaps, and per-team cost dashboards are what tell you a model has drifted — when the world changed but your model didn't. Drift detection should trigger the same pipeline that a code change does: retrain, evaluate, gate, canary. For the cost side of observability, pair this with token optimization.

---

See how MLOps connects to serving, cost, and reliability in the AI Knowledge Graph — filter to the green MLOps & CI/CD domain.