The MLOps Maturity Model
Most teams start at Level 0 — manual experiments in notebooks. Level 1 automates training pipelines. Level 2 adds CI/CD for models, automated retraining triggers, and production monitoring. Level 3 achieves full self-service ML: any engineer can ship a model with guardrails, observability, and rollback built in.
- Experiment tracking — MLflow, Weights & Biases, and Neptune log hyperparameters, metrics, and artifacts for every run.
- Feature stores — centralize and version features so training and serving use identical transformations, eliminating training-serving skew.
- Model registries — version, stage, and promote models from staging to production with full lineage tracking.
- Production monitoring — data drift detection, prediction distribution monitoring, and latency alerting catch model degradation before users notice.
Moving to dedicated GPU bare metal for our training jobs cut our experiment cycle time from 18 hours to 4 hours. That velocity change compounded — we shipped three major model updates last quarter alone.