MLOps Maturity Levels

MLOps Maturity Levels

Different organizations need different levels of MLOps. A two-person research team should not pretend to be a Fortune 500 ML platform. This reading walks through the standard maturity ladder (loosely following Google's and Microsoft's published frameworks) so you can honestly assess where you are and pick the next rung — without skipping or over-engineering.

1. Level 0 — Manual ML

The starting point for most teams.

• Data scientists train models in notebooks on their laptops.
• "Deployment" means handing a pickle file or a CSV of predictions to a backend engineer.
• No automated retraining; updates happen when someone files a JIRA.
• Reproducibility relies on hope.

Level 0 is fine for proof-of-concept work or ad-hoc analyses. It's not fine if the model is generating real-time predictions in a product.

2. Level 1 — ML Pipeline Automation

The minimum viable MLOps.

• The training process is a script (or DAG), not a notebook. It runs on a server, takes a config, produces a model.
• Code is in version control; data is too (DVC, S3 with versioning).
• Models are tracked in a registry with metadata (training data version, hyperparameters, eval metrics).
• Deployment is one command, ideally one PR-merge.
• Basic monitoring: prediction count, error rate, latency.

Most teams should aim here. Level 1 covers 80% of the value of MLOps for 30% of the cost.

3. Level 2 — CI/CD for ML

Adds continuous integration and delivery.

• Every PR triggers automated tests: unit tests, data validation, model performance on a fixed eval set.
• Continuous training: a fresh model gets trained on a schedule (weekly, daily) without human intervention.
• Continuous delivery: passing pipelines automatically deploy to a staging environment.
• Promotion to production is gated by metric thresholds and shadow tests.
• Drift monitoring with alerts.

Level 2 is what teams running multiple models in production should aim for. The investment is significant (typically a dedicated platform team) but the return is the ability to ship model improvements weekly rather than quarterly.

4. Level 3 — Self-Service ML Platform

The end-state at large organizations.

• Data scientists self-serve through a platform: launching training runs, viewing experiments, deploying models is a UI or one-line CLI.
• Feature store with reusable, versioned features.
• Standardized model serving infrastructure; swapping models is transparent to consumers.
• End-to-end lineage: any prediction can be traced back to the training run, data snapshot, and code commit that produced it.
• Multi-model deployment patterns: canaries, A/B tests, shadow mode, multi-armed bandits.
• SLOs and on-call rotations specific to ML systems.

5. The Honest Self-Assessment

Where are you? Run this checklist:

Question	If "no", you are at most…
Can a different engineer reproduce last quarter's model from scratch?	Level 0
Is the production model file traceable to a specific training run, code commit, and data version?	Level 0
Can you deploy a new model with a single command?	Level 1
Do PRs run automated tests on the model?	Level 1
Does the pipeline retrain automatically on new data?	Level 2
Are there alerts on prediction-distribution drift?	Level 2
Can a non-platform engineer launch a training run themselves?	Level 2

6. The Mistake Teams Make

7. Picking the Right Level

Org type	Right level
Solo researcher / kaggle competitor	0 with reproducibility hygiene
Startup, one model in production	1
Startup, several models, growing	1.5 — pieces of CI/CD
Mid-size company with 5-20 models	2
Tech company with 50+ models, multiple ML teams	3

The right level changes over time. Start small, instrument what you have, and add one capability per quarter when you can prove it pays for itself.