MLOps — Machine Learning Operations
MLOps applies DevOps/GitOps principles to ML: version control for data and models, automated retraining pipelines, and production monitoring beyond CPU/memory.
How MLOps Relates to What You Know
graph LR
subgraph Known["What you already know"]
CICD["CI/CD<br/>GitHub Actions · ArgoCD"]
K8S["Kubernetes<br/>Jobs · CronJobs · Operators"]
OBS["Observability<br/>Prometheus · Grafana"]
DOCKER["Docker<br/>container images · registries"]
end
subgraph MLOps["MLOps equivalents"]
EXP["Experiment Tracking<br/>MLflow · W&B<br/>≈ structured test logs with artifacts"]
PIPE["Training Pipelines<br/>Kubeflow · Airflow<br/>≈ CI pipeline but for data/models"]
DRIFT["Drift Detection<br/>Evidently · Whylogs<br/>≈ alerting but for data distribution"]
FEAT["Feature Stores<br/>Feast · Tecton<br/>≈ shared cache for ML features"]
end
DOCKER -->|model images| EXP
CICD -->|trigger training| PIPE
K8S -->|run training Jobs| PIPE
OBS -->|model metrics| DRIFT
Files
| File |
Topics |
| experiment-tracking.md |
MLflow runs/experiments/registry, W&B sweeps, artifact versioning, model promotion |
| training-pipelines.md |
Kubeflow Pipelines components/DAGs, Airflow operators, retraining triggers, K8s Jobs |
| data-drift.md |
Evidently reports, feature drift detection, retraining triggers, shadow scoring, A/B testing |
| feature-stores.md |
Feast architecture, online vs offline store, point-in-time joins, training/serving skew |
The ML Lifecycle (DevOps Analogy)
| DevOps concept |
MLOps equivalent |
git commit |
Log experiment run (params + metrics) |
| Container image tag |
Model version in registry |
| Staging environment |
Model in "Staging" registry stage |
| Production deploy |
Promote model to "Production" stage |
| CI pipeline |
Training pipeline (data → model) |
| Smoke test |
Model evaluation gate (accuracy > threshold) |
| Rollback |
Revert model registry to previous version |
| Dependency lock file |
requirements.txt + data snapshot hash |
| Feature flag |
A/B traffic split between model versions |
Learning Path
1. experiment-tracking.md → understand how models are versioned (start here)
2. training-pipelines.md → build automated training workflows on K8s
3. data-drift.md → monitor production, trigger retraining
4. feature-stores.md → advanced: shared feature layer for training/serving consistency