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The MLOps Maturity Model

Where does your team actually sit — and what is the next step?

0/1 done

Levels you can observe

A pragmatic ladder

Inspired by Google's three-level model (MLOps 0/1/2) and Microsoft's five-level one. Mapped to what learners can observe, not vendor jargon:

LevelWhat it looks likeCost of a model change
0 — Notebook heroSingle data scientist, manual deploy, no tracking.Days to weeks, often impossible to reproduce.
1 — ReproducibleTracked experiments, registered models, manual promotion.Hours, reproducible.
2 — Automated trainingCI runs train + eval on PR; promotion gated by metric thresholds.Minutes, automated.
3 — Continuous deliveryCanary + rollback on every deploy; data + model contracts tested.Minutes, safely reversible.
4 — Continuous learningRetraining triggered by drift signals; humans approve, system executes.Hands-off the happy path.

Pick the next level, not the most fashionable one. Jumping from level 0 to level 4 usually fails — and is rarely needed.

Analogy

Like the CMMI maturity ladder for software, or Bloom's taxonomy for learning. You can't skip rungs without leaving important muscle un-built.

Reflect

Self-assess.

  • ›Honestly, which level are you at right now?
  • ›What is the *one* practice that would move you up by one?
  • ›What is the cost of the move, and the cost of not moving?

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