MLOps Maturity Levels: Where Does Your Team Stand?
MLOps maturity describes how much of the path from experiment to production, and back again, a team has automated. This guide compares the two most cited models, Google's three levels and Microsoft's five, turns them into a self-assessment, and suggests the single next step that pays off most at each level.
GloryTecks Team
Training & Placement Faculty
Last updated: 8 October 2026
Most teams that build machine learning models can train one. Far fewer can retrain it next month, prove which data it was trained on, release it without a week of manual steps, and notice when it stops working. MLOps maturity is a way of describing that gap: how much of the path from experiment to production, and back, is repeatable and automated.
Two maturity models are cited more than any others, one from Google and one from Microsoft. They agree more than they differ, and together they make a practical checklist.
Google's three levels
Google's architecture guide describes MLOps as applying continuous integration, continuous delivery and continuous training to machine learning, and sets out three levels (Google Cloud: MLOps levels):
- Level 0, manual process. Every step, from data preparation to deployment, is done by hand, often in notebooks. Models are released rarely and handed over as files.
- Level 1, ML pipeline automation. The training steps become an automated pipeline, so the model can be retrained on new data, continuous training, and the pipeline itself is what gets deployed.
- Level 2, CI/CD pipeline automation. Changes to the pipeline code are built, tested and deployed automatically, so new ideas reach production quickly and safely.
Microsoft's five levels
Microsoft's model splits the same journey more finely and describes each level by people, model creation, model release and application integration (Microsoft: MLOps maturity model):
| Level | Name | What changes |
|---|---|---|
| 0 | No MLOps | Manual data gathering, untracked experiments, a model file handed over by hand |
| 1 | DevOps but no MLOps | Application releases are automated, but models are still released manually |
| 2 | Automated training | Experiments are tracked, training code and models are versioned, compute is managed |
| 3 | Automated model deployment | Releases run through a CI/CD pipeline with unit and integration tests |
| 4 | Full MLOps automated operations | Production metrics automatically trigger retraining |
Lined up, the models match closely. Google's level 0 covers Microsoft's 0 and 1. Google's level 1 is Microsoft's level 2, automated training. Google's level 2 spans Microsoft's 3 and 4.
Where does your team stand?
Answer each question honestly. The first "no" in a row marks the level you are working towards.
| Area | Question | Typical level once yes |
|---|---|---|
| Experiments | Can you find the parameters, data version and metrics of last month's best model? | 2 |
| Reproducibility | Can someone else retrain that model and get the same result? | 2 |
| Training | Does retraining run as a pipeline, without notebook steps? | 2 |
| Release | Does a model reach production through an automated pipeline with tests? | 3 |
| Rollback | Can you return to the previous model in minutes? | 3 |
| Monitoring | Would you know within a day if prediction quality dropped? | 3 to 4 |
| Retraining | Do monitored signals start retraining automatically, with checks before release? | 4 |
Notice that the people change as much as the tooling. At the lower levels data scientists, data engineers and software engineers work in isolation and hand models over. At the higher levels they convert experiments into repeatable jobs together.
The next step that pays off most
From level 0: track experiments and version data and code. Nothing else is possible until you can say exactly what produced a model. See the MLflow tutorial and data versioning with DVC.
From automated training: register models and release them through a pipeline with tests, so deployment stops depending on one person. See the model registry and CI/CD for machine learning.
From automated deployment: monitor live data and predictions, then connect alerts to retraining with gates before release. See monitoring ML models in production and continuous training.
Measuring progress
Levels are labels. Progress is easier to track with a few numbers that move as practices improve:
- Lead time: days from a finished experiment to the model serving traffic.
- Lineage coverage: the share of production models whose data, code and parameters can be traced.
- Rollback time: minutes to restore the previous model.
- Detection time: how long a drop in prediction quality goes unnoticed.
- Manual steps per release, counted honestly.
Record them once now, then again after each improvement. A team whose lead time falls from weeks to days has made real progress, whatever level it would call itself.
What not to automate early
Automation multiplies whatever it automates, including mistakes. Before automatic retraining, you need reliable monitoring, data checks and a way to compare a new model with the current one. Without them, a pipeline can quietly train on broken data and promote a worse model. Not every model needs the top level either. A model retrained once a year for an internal report may be well served by level 2.
Common mistakes
- Buying a platform before tracking and versioning the basics.
- Automating retraining before monitoring, so nobody notices when it goes wrong.
- Treating maturity as a tooling project when the main change is how teams work together.
- Aiming every model at the highest level regardless of its value or how often it changes.
- Skipping rollback, the safety net that makes frequent releases safe.
What are the levels of MLOps maturity?
What is MLOps level 0?
How do I move my team to a higher MLOps level?
Does every ML project need full MLOps automation?
Each step on this path, from experiment tracking to monitoring and continuous training, is covered in the MLOps course.
Sources
GloryTecks Team
Training & Placement Faculty
The GloryTecks faculty team mentors learners across Data Science, AI and Data Engineering, with a focus on real-time projects and placement readiness in Hyderabad.
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