What maturity levels exist in MLOps?

MLOps can be clearly divided into five maturity levels that describe an organisation's technical readiness for managing productive ML systems.

• Level 0 – No MLOps: Models are built in isolated notebooks. Data processing, training, and deployment are manual and not reproducible.
• Level 1 – DevOps without MLOps: Application code follows DevOps principles, but ML models do not. Training, versioning, and deployment are not automated and run separately from the rest of the system.
• Level 2 – Automated Training: Data and training pipelines are automated and reproducible. Experiment tracking and versioning are established; deployments are still partially manual.
• Level 3 – Automated Deployment: Models are tested via CI/CD processes and rolled out to staging and production in a controlled manner. Model registry, feature store, monitoring, and governance are integrated.
• Level 4 – Continuous Learning: Models are automatically retrained based on monitoring signals such as performance or drift and rolled out in a controlled manner. The entire lifecycle is automated, versioned, and reproducible.