Kubeflow Pipelines (KFP) Platform

A Kubeflow Pipelines (KFP) Platform is a ML workflow platform within Kubeflow.

• Context:
  • …
• Example(s):
  • Kubeflow Pipelines, v0.3.0 [1] (~2020/03/23).
  • Kubeflow Pipelines, v1.4.0 [2] (~2021/02/16)
  • …
• Counter-Example(s):
  • Airflow Platform.
• See: Argo, Argo Workflow.

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References

• https://github.com/kubeflow/pipelines

2020

• https://www.kubeflow.org/docs/pipelines/overview/pipelines-overview/
  • QUOTE: ... Kubeflow Pipelines is a platform for building and deploying portable, scalable machine learning (ML) workflows based on Docker containers. ...

    … 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 the 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.

2020

• https://towardsdatascience.com/build-your-data-pipeline-on-kubernetes-using-kubeflow-pipelines-sdk-and-argo-eef69a80237c
  • QUOTE: ... One of those services is Kubeflow Pipelines (KFP), which is a platform for building and deploying portable, scalable machine learning (ML) workflows based on Docker containers. It has a user interface for managing and tracking experiments, jobs, and runs. There is a subtle distinction between Argo Workflows and KFP. Argo is the workflow engine behind KFP and KFP is meant mainly for ML- related usages.

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