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MLflow and Kubeflow offer distinct features and advantages for managing machine learning workflows. MLflow is lightweight and user-friendly, primarily focused on model tracking and artifact management. Kubeflow is a more extensive ecosystem, providing end-to-end pipeline capabilities for machine learning tasks. The choice between the two depends on the organization's requirements, team expertise, and infrastructure preferences. Understanding the trade-offs, maturity, and stability of the tools is crucial for successful adoption and integration into production workflows.