Kubeflow Pipelines

Overview

Kubeflow Pipelines is a platform for building and deploying portable, scalable machine learning pipelines on Kubernetes. It provides a UI and Python SDK to define, schedule, and monitor pipeline runs.

Best for

Best for
Teams already using Kubeflow who need a managed way to orchestrate ML pipelines on Kubernetes

Use cases

• Building end-to-end ML training and evaluation pipelines
• Automating model retraining and deployment workflows
• Orchestrating multi-step data processing and feature engineering

Notes

Kubeflow Pipelines is a platform for building and deploying portable, scalable machine learning pipelines on Kubernetes. It provides a UI and Python SDK to define, schedule, and monitor pipeline runs.

4,151 stars on GitHub. Last updated 2026-06-01. Licensed Apache-2.0.

Use cases

• Building end-to-end ML training and evaluation pipelines
• Automating model retraining and deployment workflows
• Orchestrating multi-step data processing and feature engineering

Pros

• Open source with strong integration into the Kubeflow ecosystem
• Scalable pipeline execution on Kubernetes clusters
• Provides a visual dashboard for tracking pipeline runs and artifacts

Cons

• Requires Kubernetes expertise to set up and maintain
• Steep learning curve for defining complex pipelines
• Community-driven support with limited official documentation

Indexed from awesome-llmops and enriched against its public facts.