Checklist della maturità MLOps

MLOps maturity is a ladder from fully manual to fully automated. Knowing your rung tells you exactly what to improve next. 📊

At Level 0 everything is by hand: notebooks, manual training, and deploys done by copying files. It works, until it suddenly does not.

Manual means slow, fragile, and unrepeatable. One person leaves and the model becomes a mystery nobody can rebuild.

At Level 1 the training pipeline is automated. You can retrain on new data with a command, not a week of manual steps.

At Level 2 you add CI/CD: code and models are tested and deployed automatically on every change, with humans only approving.

First checklist item: can you version your data, code, and model together, so any past result can be rebuilt exactly?

Next: do you have automated tests for data and models? Tests catch broken pipelines before they ever reach production.

Then: are live models monitored? If you cannot see accuracy or drift in production, you are flying blind after deploy.

Also: is a run reproducible? Pinned environments and seeds mean the same inputs always give the same model, anywhere.

You do not need full automation on day one. Match your maturity to your needs; a tiny project rarely justifies a Kubernetes cluster.

Climb the ladder gradually. Pick the weakest checklist item and fix that first, rather than rebuilding everything at once.

Let us place a team on the MLOps maturity ladder.

Maturity runs from manual to automated, and the checklist shows where to climb: versioning, tests, monitoring, reproducibility. Improve one rung at a time. 🪜