Kubeflow is a Kubernetes machine learning platform and containerized toolkit designed to orchestrate the entire machine learning lifecycle. It functions as an MLOps workflow orchestrator and infrastructure layer for building, training, and deploying models within containerized environments.
The project provides specialized infrastructure for scaling compute resources and managing GPU workloads for large-scale distributed training. It automates the transition of models from experimental development to production through workflow orchestration and model deployment services.
The platform covers a broad range of capabilities including containerized development, distributed training, and model serving. It utilizes native orchestration to manage machine learning lifecycles, ensuring that data preparation and training are integrated into scalable production pipelines.