Beginner

Introduction to KubeFlow Pipelines

Understand what KubeFlow Pipelines is, its architecture, how it orchestrates ML workflows on Kubernetes, and where it fits in the modern MLOps ecosystem.

What is KubeFlow Pipelines?

KubeFlow Pipelines (KFP) is a platform for building and deploying portable, scalable machine learning workflows based on containers. It provides a Python SDK for defining pipelines, a UI for managing and tracking experiments, and an execution engine that runs workflows on Kubernetes.

KFP is part of the broader KubeFlow ecosystem, but it can also be deployed standalone for teams that only need pipeline orchestration.

Architecture Overview

KubeFlow Pipelines consists of several key components:

Why KubeFlow Pipelines?

KFP solves critical challenges in ML workflows:

• Reproducibility: Every pipeline run is recorded with its parameters, code versions, and outputs, making experiments fully reproducible.
• Portability: Pipelines run on any Kubernetes cluster - on-premises, GKE, EKS, or AKS - without modification.
• Scalability: Each pipeline step runs in its own container and can request specific resources (GPUs, memory, storage).
• Reusability: Components can be shared across pipelines and teams, reducing duplication and ensuring consistency.
• Automation: Pipelines can be triggered by schedules, events, or CI/CD systems for continuous training.

KFP vs Other Orchestrators

Feature	KubeFlow Pipelines	Apache Airflow	Prefect	MLflow
ML-focused	✓ Native	General purpose	General purpose	✓ Native
Kubernetes-native	✓	Optional	Optional	Optional
Container isolation	✓ Per step	✓ Per task	Optional	Optional
Experiment tracking	✓ Built-in	Limited	Limited	✓ Core feature
Artifact management	✓ Native	Limited	Limited	✓ Native
GPU support	✓ Native	Via K8s executor	Via K8s	Via K8s