eterna2/ml-pipelines-frontend repository overview

⁠Overview of the Kubeflow pipelines service

Kubeflow⁠ is a machine learning (ML) toolkit that is dedicated to making deployments of ML workflows on Kubernetes simple, portable, and scalable.

Kubeflow pipelines are reusable end-to-end ML workflows built using the Kubeflow Pipelines SDK.

The Kubeflow pipelines service has the following goals:

• End to end orchestration: enabling and simplifying the orchestration of end to end machine learning pipelines
• Easy experimentation: making it easy for you to try numerous ideas and techniques, and manage your various trials/experiments.
• Easy re-use: enabling you to re-use components and pipelines to quickly cobble together end to end solutions, without having to re-build each time.

⁠Documentation

Get started with your first pipeline and read further information in the Kubeflow Pipelines documentation⁠.

⁠Blog posts

• Getting started with Kubeflow Pipelines⁠ (By Amy Unruh)
• How to create and deploy a Kubeflow Machine Learning Pipeline (By Lak Lakshmanan)
  • Part 1: How to create and deploy a Kubeflow Machine Learning Pipeline⁠
  • Part 2: How to deploy Jupyter notebooks as components of a Kubeflow ML pipeline⁠

⁠Acknowledgments

Kubeflow pipelines uses Argo⁠ under the hood to orchestrate Kubernetes resources. The Argo community has been very supportive and we are very grateful.