Integrating traditional DevOps tools into MLOps pipelines is effective for automating version control, CI/CD, containerization, and deployment, ensuring reproducibility and faster iteration. Tools like Git, Jenkins, Docker, and Kubernetes handle code, workflows, and scalable deployments, but MLOps also requires specialized solutions for data versioning, model training, feature management, and performance monitoring. Combining DevOps automation with ML-specific tools like MLflow, DVC, or feature stores enables end-to-end management of the ML lifecycle, ensuring models are reliable, scalable, and continuously improved in production.