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Kubeflow is an open-source platform designed to facilitate the development, orchestration, deployment, and running of scalable and portable machine learning workflows on Kubernetes. Its primary aim is to make machine learning easy to deploy and manage on Kubernetes, allowing data scientists and ML engineers to focus on the model-building instead of the infrastructure. With a variety of native Kubernetes resources and APIs, Kubeflow supports different machine learning frameworks such as TensorFlow, PyTorch, and MXNet, thereby offering runtime flexibility for developers.

Key features of Kubeflow include a user-friendly interface for building end-to-end machine learning workflows, support for hyperparameter tuning, efficient model serving, and a robust architecture that allows teams to scale their machine learning solutions seamlessly. Organizations across various industries choose Kubeflow because it integrates tightly with Kubernetes, enabling them to leverage cloud-native practices while enhancing collaborative workflows among data scientists and engineers. The open-source nature of Kubeflow also fosters a vibrant community, continuously contributing to its development and offering shared solutions to common machine learning challenges. Being Kubernetes native, Kubeflow can run on cloud or on-premise environments, making it flexible regarding deployment options.