Kubeflow - machine learning on kubernetes

Kubeflow is an open-source platform for deploying, orchestrating, and managing machine learning workflows on Kubernetes. Designed to make ML workloads portable, scalable, and composable, Kubeflow provides a comprehensive MLOps solution for running ML pipelines in production. Key components include Kubeflow Pipelines for workflow orchestration, Jupyter notebooks for experimentation, Katib for hyperparameter tuning, and KServe for model serving. The platform supports distributed training, multi-user isolation, and integration with popular ML frameworks like TensorFlow, PyTorch, and scikit-learn. Kubeflow enables teams to build reproducible, scalable ML workflows that can run across different environments from on-premise clusters to major cloud providers. With features like experiment tracking, model versioning, and automated deployment pipelines, Kubeflow helps organizations implement best practices for MLOps at scale. The platform is particularly valuable for enterprises that need to manage complex ML workflows, ensure compliance, and scale ML operations across teams while leveraging Kubernetes infrastructure.