kubeflow/pipelines

What it solves

Kubeflow Pipelines simplifies the deployment and orchestration of end-to-end machine learning (ML) workflows on Kubernetes. It addresses the complexity of managing ML experiments, ensuring that workflows are portable and scalable across different environments.

How it works

The system uses a dedicated SDK to build reusable components and pipelines. These workflows are orchestrated using the Argo Emissary executor, which allows the service to manage Kubernetes resources independently of the container runtime. It can be installed either as part of the full Kubeflow platform or as a standalone service.

Who it’s for

ML engineers and data scientists who need to build, manage, and scale ML pipelines that are portable and reusable across Kubernetes clusters.

Highlights

• End-to-end orchestration: Simplifies the management of complex ML workflows from start to finish.
• Easy experimentation: Streamlines the process of trying different techniques and managing multiple trials.
• Reusable components: Allows users to quickly assemble solutions by reusing existing pipeline components without rebuilding them from scratch.