Reusing DevOps tools in MLOps workflows is highly effective for automating version control, CI/CD pipelines, containerization, and deployment, ensuring reproducibility and faster iteration of machine learning models. Tools like Git, Jenkins, Docker, and Kubernetes help streamline code integration, model packaging, and scalable deployment, bringing core DevOps benefits to ML projects. However, gaps remain that require MLOps-specific solutions, such as data versioning, feature management, model training orchestration, performance monitoring, and drift detection. Tools like MLflow, Kubeflow, or DVC address these gaps by managing datasets, tracking experiments, monitoring model accuracy, and triggering retraining workflows. Combining standard DevOps automation with these ML-focused tools ensures a complete MLOps lifecycle, enabling models to be reliably deployed, monitored, and continuously improved in production.