Kubeflow is primarily used in MLOps to build, deploy, and manage end-to-end machine learning workflows on Kubernetes, enabling teams to automate and scale complex ML pipelines in a cloud-native environment. It supports tasks such as data preparation, model training, hyperparameter tuning, deployment, and monitoring, making ML processes more consistent and reproducible across different environments. By integrating tightly with Kubernetes, it ensures scalability, portability, and efficient resource utilization for machine learning workloads. In my opinion, the most important capability is end-to-end ML workflow automation, because Kubeflow’s main strength lies in connecting all stages of the ML lifecycle into a single automated pipeline, which improves efficiency, reduces manual effort, and accelerates model development and deployment.