Kubeflow Pipelines is a platform for building and deploying portable, scalable machine learning (ML) workflows based on Docker containers.
We offer to flavors: Kubeflow pipelines based on Argo Workflows and based on TektonCD engine
#To install KF based on Argo Workflowsk3ai apply kubeflow-pipelines#To install KF based on TektonCDk3ai apply kf-pipelines-tekton
The Kubeflow Pipelines platform consists of:
A user interface (UI) for managing and tracking experiments, jobs, and runs.
An engine for scheduling multi-step ML workflows.
An SDK for defining and manipulating pipelines and components.
Notebooks for interacting with the system using an SDK.
The following are the goals of Kubeflow Pipelines:
End-to-end orchestration: enabling and simplifying the orchestration of 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 create end-to-end solutions without having to rebuild each time.
Learn more on the Kubeflow website: https://www.kubeflow.org/docs/pipelines/****