MLOps maturity levels: the most well-known models
- Edwin Kuss
- 6 min
Why understanding the levels of maturity in ML is important

Like most IT and DevOps processes, MLOps has defined maturity levels that reflect how advanced an organization is in managing its machine learning workflows. These maturity levels help teams assess where they currently stand in their AI/ML development journey and what gaps they need to close to progress toward full automation, scalability, and governance.
Understanding your MLOps maturity level is not just a diagnostic exercise — it’s a strategic roadmap for growth. Companies use these models to benchmark their operational efficiency, identify bottlenecks in data pipelines or model deployment, and align their ML practices with business goals.
By adopting a recognized MLOps maturity framework, organizations can:
- Evaluate their current capabilities in data management, experiment tracking, monitoring, and model lifecycle automation.
- Prioritize investments in tools and infrastructure that accelerate model reproducibility, compliance, and collaboration.
- Compare progress against industry peers, helping determine competitive position and readiness for large-scale AI adoption.
Ultimately, MLOps maturity models provide a clear, measurable pathway toward building resilient, automated, and production-ready ML systems that deliver consistent business value.
Google Model
Google introduced one of the earliest and most widely recognized MLOps maturity models, providing a straightforward three-level framework for understanding how machine learning operations evolve. Despite its simplicity, the model effectively captures the gradual shift from manual experimentation to fully automated ML pipelines.
Level 0 — Manual process
At this initial stage, most activities are performed manually. Data scientists experiment locally, training and testing models using ad-hoc scripts without standardized workflows or automation. There is minimal version control, limited reproducibility, and a strong dependency on individual contributors. As a result, collaboration and scaling become difficult, and deployment to production often involves manual handoffs to engineering teams.
Level 1 — ML pipeline automation
Organizations at this level begin to automate portions of the ML workflow, such as data ingestion, model training, and evaluation. Automated pipelines replace many manual steps, improving reproducibility and efficiency. Teams start implementing experiment tracking and basic model versioning, but the process may still lack full integration with deployment or monitoring systems.
Level 2 — CI/CD pipeline automation
The highest level in Google’s model represents end-to-end automation. Continuous Integration and Continuous Delivery (CI/CD) principles are fully applied to machine learning pipelines. Models can be retrained, validated, and deployed automatically when new data becomes available. Monitoring, testing, and rollback mechanisms are also integrated, ensuring reliability and consistency throughout the ML lifecycle.
While concise, Google’s three-level approach effectively demonstrates how organizations can evolve from manual, script-driven experimentation to scalable, production-ready machine learning pipelines. It remains a practical reference point for teams beginning their MLOps journey.
Azure Model
Microsoft Azure proposes a more granular approach to assessing MLOps maturity, expanding the framework to five distinct levels. This model provides a structured roadmap that helps organizations evaluate how advanced their ML operations are and identify the next steps toward full automation and governance.
Level 0 — No MLOps
At this starting point, machine learning workflows are fragmented and mostly manual. Teams rely on isolated notebooks, untracked datasets, and ad-hoc scripts. Collaboration is minimal, and models are difficult to reproduce or deploy consistently.
Level 1 — DevOps but no MLOps
Here, organizations have adopted DevOps practices for software delivery, such as source control and CI/CD, but these processes have not yet been extended to machine learning. ML development still happens in silos, separate from production workflows.
Level 2 — Automated training
Teams begin automating the training and validation stages of their ML pipelines. Data ingestion, preprocessing, and model retraining become repeatable processes, often triggered by new data availability. Experiment tracking and dataset versioning start to appear, improving reliability and transparency.
Level 3 — Automated model deployment
At this stage, model deployment becomes streamlined. Models move automatically from testing to production environments through integrated CI/CD pipelines. Monitoring systems start to capture key performance metrics, and rollback procedures can be executed when performance declines.
Level 4 — Full MLOps automated operations
The highest maturity level represents a fully automated, self-sustaining ML ecosystem. Continuous integration, delivery, and monitoring are seamlessly connected. Models are retrained and redeployed automatically, with governance, compliance, and monitoring tightly embedded in the workflow. This stage ensures that machine learning delivers consistent, scalable, and business-ready results.
The Azure MLOps maturity model stands out for its clarity and practicality — it illustrates how teams can progress from fragmented experimentation to continuous, automated ML delivery that aligns technical performance with business outcomes.
GigaOm Model
The GigaOm MLOps Maturity Model, developed by the analytical firm GigaOm, is widely regarded as one of the most detailed and comprehensive frameworks available today. Inspired by the Capability Maturity Model Integration (CMMI) — a well-known approach to process improvement — this model also defines five levels of maturity, ranging from Level 0 to Level 4.

* image source: https://research.gigaom.com/report/delivering-on-the-vision-of-mlops
**The five maturity levels of the GigaOm MLOps Model
The diagram illustrates how organizations progress from ad hoc experimentation (Level 0) to fully optimized and governed MLOps practices (Level 4). Each stage reflects increasing sophistication in strategy, architecture, modeling, processes, and management.
Unlike simpler models, GigaOm’s framework takes a holistic view of MLOps by assessing maturity across five key dimensions: strategy, architecture, modeling, processes, and management. This multi-layered perspective allows organizations to evaluate not just their technical automation, but also how well their ML initiatives align with business objectives, operational standards, and organizational culture.
At lower levels, machine learning practices tend to be ad hoc and experimental, with limited structure or long-term vision. As organizations move up the maturity scale, they establish formalized processes, scalable architectures, and governance mechanisms. The highest level represents a fully optimized, data-driven organization where MLOps practices are embedded into every stage of the ML lifecycle — from data collection and model design to deployment, monitoring, and continuous improvement.
Using the GigaOm model early in the development and implementation of ML systems helps companies identify gaps before they become obstacles. It enables teams to plan strategically, adopt best practices in data management and workflow automation, and significantly reduce the risk of failure during scaling or production phases.
In essence, the GigaOm model serves as both a diagnostic and a roadmap, guiding organizations toward more structured, repeatable, and business-aligned MLOps operations.

Moving toward mature and scalable MLOps
Although each framework defines its own stages, all MLOps maturity models describe the same evolution — from isolated, manual experiments to fully automated and governed machine learning operations.
What distinguishes them is the angle of focus.
Google’s model highlights technical automation through CI/CD integration.
Azure’s model adds finer granularity, emphasizing training and deployment automation.
GigaOm’s framework broadens the perspective, connecting technical progress with strategic alignment, governance, and process maturity.
Together, they show that MLOps maturity is not just about automation — it’s about creating a sustainable, transparent, and collaborative ecosystem for machine learning. Knowing where your organization stands on this path helps you plan realistic improvements, invest wisely in infrastructure, and accelerate your AI initiatives without losing control or compliance.
In the modern landscape, MLOps maturity isn’t a finish line — it’s a continuous journey toward smarter, faster, and more accountable AI development.