MLOps Maturity Levels: A Roadmap from Level 0 to 4
MLOps maturity determines how fast and reliably your models reach production. The levels in Microsoft's and Google's models, what characterises each level and the next step.
Read more →MLOps Maturity Assessment
How fast and reliably do your models reach production? This assessment determines your MLOps level across experiment tracking, training pipelines, deployment, monitoring, LLMOps and team processes.
- 4 min
- 12 questions
- Data science, ML engineering and platform teams
What does it measure?
- Experiment tracking and versioning
- Training pipelines and reproducibility
- Deployment and rollback
- Monitoring and retraining
- LLMOps: prompt management and evaluation
- Team, roles and documentation
How is it scored?
For each of 12 questions you pick the option closest to your current practice (0–3 points). The total is converted to a percentage and mapped to Microsoft's widely used five-level MLOps maturity model (Levels 0–4). Results by dimension show your weakest areas.
Frequently Asked Questions
What do the levels mean?
Level 0: no MLOps · Level 1: DevOps but no MLOps · Level 2: automated training · Level 3: automated model deployment · Level 4: full MLOps automated operations.
Does every organisation need Level 4?
No. The right level depends on the number of models, how often they change and the risk involved. For an organisation with a few models, Levels 2–3 are often enough.
We have LLM applications but no classic models. Does this apply?
Yes. The LLMOps questions are directly for you; for the others, answer with your LLM applications' development and deployment processes in mind.
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