🔁Kubeflow Pipelines

NEW

A Kubernetes-native system to define, orchestrate, and monitor reproducible machine learning workflows as pipelines.

DESCRIPTION

Kubeflow Pipelines provides a containerized pipeline framework for authoring, orchestrating, and tracking machine learning workflows on Kubernetes. It offers a UI, SDK, and metadata tracking for reproducibility, lineage, and experiment management. Pipelines are defined as DAGs of components with parameterization and caching. It integrates with Argo Workflows or Tekton, supports multi-step ML tasks, and enables scalable execution using Kubernetes resources. Common use cases include data preprocessing, model training, evaluation, and deployment steps.

ALTERNATIVE DEFINITIONS

Kubeflow Documentation

Kubeflow Pipelines is a platform for building and deploying portable, scalable machine learning (ML) workflows based on Docker containers.

Emphasizes portability, scalability, and container-based workflows.

Source

Kubeflow Documentation

Kubeflow Pipelines includes a user interface (UI) for managing pipelines, an SDK for developing pipelines and components, and a backend for executing pipelines and storing metadata.

Highlights system components: UI, SDK, and backend metadata store.

Source

RELATED TERMS

Kubernetes MLflow TensorFlow Extended MLOps Feature Store Model Registry Continuous Integration and Continuous Delivery for Machine Learning Vertex AI Pipelines

COMPETING TERMS

Apache Airflow Argo Workflows Metaflow

RELATIONS

• Broader: Pipelines

KNOWLEDGE AREAS

MLOps Platform & Tooling AI Infrastructure Software Engineering

TARGET AUDIENCE

MLOps Engineer AI Engineer Data Scientist DevOps Engineer Cloud Architect

EXTERNAL SOURCES

• Wikipedia - Kubeflow
• Kubeflow - Official Kubeflow Pipelines documentation.
• Kubeflow - V1 Pipelines introduction and concepts.
• Argo Project - Workflow engine used by Pipelines.

TOP QUESTIONS

Why use Kubeflow Pipelines for MLOps?

It offers reproducible containerized workflows on Kubernetes, built-in metadata tracking, caching, and an interface for experiments and pipeline runs.

Category: Relevancy

How are Kubeflow Pipelines defined?

Pipelines are DAGs composed of containerized components with typed inputs/outputs, authored via the Python SDK and compiled to YAML.

Category: Requirements

What storage does Kubeflow Pipelines use for metadata?

The backend stores pipeline runs, artifacts, and lineage in a metadata store, enabling experiment tracking and reproducibility.

Category: Example

How does Kubeflow Pipelines compare to Apache Airflow?

Airflow is general workflow orchestration; Kubeflow Pipelines targets ML workflows with metadata, artifacts, and reproducibility features.

Category: Alternatives

What is a common mistake when deploying Kubeflow Pipelines?

Underestimating Kubernetes cluster resources, storage, and RBAC permissions leads to failing runs and unstable pipelines.

Category: Mistakes

Can Kubeflow Pipelines run on-premises?

Yes, it runs on any Kubernetes cluster, including on-prem and managed cloud Kubernetes services.

Category: Example

What is currently a hot topic around Kubeflow Pipelines?

Integration with ML metadata stores, feature stores, and model registries, plus Tekton backend and caching improvements.

Category: Topic

How do you monitor Kubeflow Pipelines?

Use the Kubeflow UI, logs, metrics integrations like Prometheus/Grafana, and event hooks for alerts.

Category: Management

What are the risks of using Kubeflow Pipelines?

Operational complexity of Kubernetes and multi-component dependencies can raise maintenance burdens and learning curve.

Category: Risks