The three levels of MLOps maturity represent an organization's progression in managing machine learning operations. Level 0 is characterized by a lack of formal MLOps practices, relying on ad-hoc, manual processes for model development and deployment, which results in inefficiencies and higher risk. Level 1 introduces manual automation in parts of the ML pipeline, such as model training and version control, but still requires manual intervention for deployment and monitoring. Level 2 is marked by full automation of the ML pipeline, including continuous integration and delivery (CI/CD), automated model testing, and real-time monitoring of model performance. Collaboration between teams is solidified, and the process becomes scalable, reliable, and agile. To move toward higher maturity levels, organizations should focus on automating manual tasks, enhancing cross-team collaboration, and integrating advanced monitoring tools for continuous improvement and scalability.