Our team aligns with Google's MLOps principles by integrating machine learning, DevOps, and data engineering to streamline the deployment and maintenance of ML systems in production. We emphasize automation at every stage, from data preprocessing and model training to deployment and monitoring. We use versioning tools like Git and DVC (Data Version Control) to track model and data changes, ensuring reproducibility and consistency across experiments. Continuous integration and deployment (CI/CD) pipelines are in place to automatically test and deploy models, with automated retraining triggered by new data or model performance degradation. Monitoring tools like Prometheus and Grafana help us track model performance and detect drift in real time, enabling proactive adjustments. In terms of maturity, our workflows are approaching an advanced stage, with automated pipelines and robust monitoring in place, but there are still areas for improvement, such as further enhancing the automation of retraining processes and optimizing model governance practices. Overall, we are in the advanced stage of MLOps maturity, focused on continuous improvement.