# Kubeflow Pipelines

**Canonical:** https://apis.io/providers/kubeflow-pipelines/  
**Website:** https://www.kubeflow.org/docs/components/pipelines/  
**APIs profiled:** 8

Kubeflow Pipelines is a platform for building and deploying portable, scalable machine learning workflows based on Docker containers. It provides a way to orchestrate complex ML workflows with dependencies, enabling data scientists and ML engineers to deploy production-ready ML systems on Kubernetes.

## Kin Score — 34.1 / 100 (thin)

Scored 2026-08-25 under rubric 0.14.0. Trend: flat (+0.0 from 34.1).

| Facet | Score |
|---|---|
| Discoverability | 64.8 |
| Contract Quality | 46.9 |
| Governance | 0.0 |
| Contract Governance | 0.0 |
| Operational Transparency | 39.5 |
| Developer Ergonomics | 38.1 |
| Commercial Clarity | 15.8 |
| Access Clarity | 15.8 |

## Agent readiness — 19.8 (agent-aware)

| Dimension | Value |
|---|---|
| Spec Presence | yes |
| Agentic Access | derived |
| Reversibility Documented | no |
| MCP Server | no |
| Auth Clarity | bearer |
| Idempotency | no |
| Error Semantics | no |
| OpenAPI Examples | no |
| Rate Limit Signal | documented |
| Event Surface Described | no |
| Agent Skills | no |
| Well Known Catalog | no |
| Consent Identity | no |
| Agent Card | no |
| Dry Run Mode | no |
| Delegated Identity | no |
| Protected Resource Metadata | no |
| Dynamic Client Registration | no |
| Agentic Commerce | no |

## Access

Freemium · Self-serve signup — onboarding: self-serve, pricing: freemium, trial: no (confidence: high).

## APIs (8)

- **Kubeflow Pipelines Python SDK** — Python SDK for building, compiling, and submitting ML pipelines. Provides decorators and utilities to define pipeline components and workflows using Python.
- **Kubeflow Pipelines Go Client** — Go client library for interacting with the Kubeflow Pipelines API programmatically from Go applications.
- **Kubeflow Pipelines Metadata API** — API for tracking and managing metadata about ML artifacts, executions, and lineage information throughout the ML pipeline lifecycle, backed by ML Metadata (MLMD).
- **Kubeflow Pipelines Experiments API** — Group runs and recurring jobs into experiments
- **Kubeflow Pipelines Health API** — Health and auth checks
- **Kubeflow Pipelines Pipelines API** — Manage pipeline definitions
- **Kubeflow Pipelines PipelineVersions API** — Manage versions of pipelines
- **Kubeflow Pipelines Runs API** — Pipeline runs

## Agentic access (1)

- **Kubeflow Pipelines Agentic Access** — 13 operations · 6 acting

## Security (2)

- **Kubeflow Pipelines Authentication** — http · 1 scheme
- **Kubeflow Pipelines Domain Security** — TLSv1.3 · HSTS · DNSSEC

## Plans (1)

- **Kubeflow Pipelines Plans Pricing**

## Tags

Data Science, Kubernetes, Machine-Learning, MLOps, Orchestration, Pipelines, Workflows

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Profiled by [API Evangelist](https://apievangelist.com) and published on [APIs.io](https://apis.io/providers/kubeflow-pipelines/). Scores are computed from the provider's own public artifacts under a published rubric.