google-cloud-pipeline-components
This SDK enables a set of First Party (Google owned) pipeline components that allow users to take their experience from Vertex AI SDK and other Google Cloud services and create a corresponding pipeline using KFP or Managed Pipelines.
Decision gist · record as of 2026-08-14
Yes, if you are building ML workflows on Google Cloud Vertex AI and want to avoid writing custom pipeline orchestration code. The library is actively maintained, has no security vulnerabilities, uses a permissive license, and integrates cleanly with the Kubeflow Pipelines SDK. Install only if you have a GCP project with Vertex API enabled and authenticated credentials; it is not useful without Google Cloud infrastructure.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >= 3.8, a Google Cloud project with Vertex API enabled, and authenticated GCP credentials.
- Low friction installation as a pure-Python wheel.
- Actively maintained with recent releases; repo shows 4183 stars and last commit on 2026-08-14.
License · maintenance · safety
Apache License 2.0 (permissive) — Apache License 2.0 (permissive) allows commercial and private use with minimal restrictions; suitable for most production environments.
last release 2025-11-10 (277 days) · last repo commit 2026-08-14 · 4,183 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,397,267 downloads/mo, #3,958 on PyPI
Alternatives
Verify before relying
pip install google-cloud-pipeline-components
from google_cloud_pipeline_components import aiplatform
from kfp import dsl
@dsl.pipeline
def my_pipeline():
task = aiplatform.AutoMLTabularTrainingJobRunOp(...)- Whether all Vertex AI services used by components are available in all GCP regions.
- Performance characteristics when composing many components into a single pipeline.
- Backward compatibility guarantees across major version updates.
What it is and what it does
Google Cloud Pipeline Components is a library of pre-built Kubeflow Pipelines components that wrap Google Cloud Vertex AI services. Instead of writing custom pipeline logic to call Vertex AI APIs, you import components like AutoML trainers, model deployers, and data processors, then compose them together using the KFP SDK to create end-to-end ML workflows. The components handle authentication, API calls, and job orchestration on your behalf.
The package is designed for teams already using Vertex AI who want to build reproducible, version-controlled pipelines without reinventing integration code. It supports tabular, image, and text AutoML tasks, model training, deployment, and batch prediction. Maintenance is active, Python support covers versions 3.8 through 3.13, and it has no known security vulnerabilities.
Use it for
- Build an AutoML image classification pipeline that trains, evaluates, and deploys a model end-to-end on Vertex AI.
- Compose tabular data preprocessing, AutoML training, and model deployment into a single reproducible workflow.
- Orchestrate batch predictions across multiple models using Vertex AI Pipelines without custom API integration code.
- Create reusable ML pipeline templates for your team using pre-validated Vertex AI components.
- Integrate Vertex AI model training with external data sources and post-processing steps in a managed pipeline.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building ML workflows on Google Cloud Vertex AI and want to avoid writing custom pipeline orchestration code.
The library is actively maintained, has no security vulnerabilities, uses a permissive license, and integrates cleanly with the Kubeflow Pipelines SDK. Install only if you have a GCP project with Vertex API enabled and authenticated credentials; it is not useful without Google Cloud infrastructure.
Install
google-cloud-pipeline-components on PyPI
Before you install
Low friction installation as a pure-Python wheel. Actively maintained with recent releases; repo shows 4183 stars and last commit on 2026-08-14. Requires Google Cloud project setup and authentication outside the package itself.
Requires Python >= 3.8, a Google Cloud project with Vertex API enabled, and authenticated GCP credentials.
License in practice
Apache License 2.0 (permissive) allows commercial and private use with minimal restrictions; suitable for most production environments.
Quickstart
pip install google-cloud-pipeline-components
from google_cloud_pipeline_components import aiplatform
from kfp import dsl
@dsl.pipeline
def my_pipeline():
task = aiplatform.AutoMLTabularTrainingJobRunOp(...)Verify before relying
- Whether all Vertex AI services used by components are available in all GCP regions.
- Performance characteristics when composing many components into a single pipeline.
- Backward compatibility guarantees across major version updates.
Package facts
| License | Apache License 2.0 permissive |
| Python support | Supports the current Python release >=3.8.0 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packagesgoogle-api-corekfpgoogle-cloud-aiplatformJinja2 |
| Maintenance | Actively maintained 277 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,397,267 / month, #3,958 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseOperating System :: MacOSOperating System :: UnixProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Software DevelopmentTopic :: Software Development :: LibrariesTopic :: Software Development :: Libraries :: Python Modules |
Evidence: google_cloud_pipeline_components-2.22.0-py3-none-any.whl
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See also kfp · kfp-pipeline-spec · kubeflow · kfp-kubernetes · cloud-accelerator-diagnostics · kfp-server-api · azureml-pipeline-steps · langchain-google-vertexai · google-cloud-aiplatform · prefect-gcp