Our team integrates MLOps and DevOps practices to create a seamless, efficient workflow that supports both traditional software development and machine learning operations. We adopt DevOps principles like continuous integration (CI) and continuous delivery (CD) to automate code integration, testing, and deployment processes for our software applications. For machine learning workflows, we apply MLOps practices such as automated model training, version control for data and models, and model deployment pipelines using tools like Kubeflow and MLflow. By integrating these practices, we automate the entire machine learning lifecycle, from training and validation to deployment and monitoring in production, ensuring that models are continuously updated and optimized. Model drift detection and performance monitoring are embedded into the pipeline to proactively identify when models need retraining, just as DevOps ensures continuous software delivery and system health. This integration has allowed us to streamline both our software and machine learning workflows, improving efficiency, reducing manual effort, and ensuring that both our applications and models are always up to date and performing optimally.