Kubeflow Pipelines
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.
Kubeflow Pipelines publishes 5 APIs on the APIs.io network, including Experiments API, Health API, Pipelines API, and 2 more. Tagged areas include Data Science, Kubernetes, Machine Learning, MLOps, and Orchestration.
Kubeflow Pipelines’ developer surface includes authentication, documentation, getting-started guide, engineering blog, changelog, and 7 more developer resources.
Kin Score
APIs 8
Individual APIs this provider publishes, each with its own machine-readable definition.
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
Scroll for all 8
Open Collections 1
Open, tool-agnostic API collections (OpenAPI-derived and Bruno).
Kubeflow Pipelines REST API
OPEN COLLECTIONPricing Plans 1
Published pricing tiers and plan structures.
Rate Limits 1
Documented rate limits and quota policies.
Kubeflow Pipelines Rate Limits
RATE LIMITSFinOps 1
Cost, billing, and metering signals for API financial operations.
Security Posture 2
Authentication, domain security, vulnerability disclosure, and trust-center signals.
Agentic Access 1
Recommended x-agentic-access execution contracts for AI agents.
Resources
Get Started 1
Portal, sign-up, and the first successful call
Documentation 1
Reference material describing how the API behaves
Agent Surfaces 1
MCP servers, agent skills, and machine-readable catalogs
Build 2
SDKs, sample code, and the tooling you integrate with
Access & Security 2
Authentication, authorization, and security posture
Operate 2
Status, limits, changes, and where to get help
Company 3
The organization behind the API