google-cloud-automl

Google Cloud Automl API client library

google-cloud-automl Permissive license Apache-2.0 Active ★ 5,370 v2.20.0 released 2026-06-03

Install

google-cloud-automl on PyPI

pip

pip install google-cloud-automl

uv

uv add google-cloud-automl

poetry

poetry add google-cloud-automl

Package facts

License	Apache-2.0 (permissive)
Python support	supports the current Python release (>=3.10)
Install friction	low — pure-Python wheel
Runtime dependencies	5 — google-api-core, google-auth, grpcio, proto-plus, protobuf
Maintenance	actively maintained — 71 days since the last release
Last repo commit	2026-08-13
First released	2018-07-18
Popularity	one of the top 1,000 most-downloaded packages on PyPI (30-day window, as of 2026-08-13)
Known vulnerabilities	none known (OSV.dev, checked 2026-08-13)

Evidence: google_cloud_automl-2.20.0-py3-none-any.whl

Development Status :: 5 - Production/StableIntended Audience :: DevelopersLicense :: OSI Approved :: Apache Software LicenseOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Internet

About google-cloud-automl

from the package's own PyPI description — quoted content, verbatim

Python Client for Cloud AutoML

Cloud AutoML_: Train high-quality custom machine learning models with minimum effort and machine learning expertise.

Client Library Documentation_
Product Documentation_

.. |stable| image:: https://img.shields.io/badge/support-stable-gold.svg :target: https://github.com/googleapis/google-cloud-python/blob/main/README.rst#stability-levels .. |pypi| image:: https://img.shields.io/pypi/v/google-cloud-automl.svg :target: https://pypi.org/project/google-cloud-automl/ .. |versions| image:: https://img.shields.io/pypi/pyversions/google-cloud-automl.svg :target: https://pypi.org/project/google-cloud-automl/ .. _Cloud AutoML: https://cloud.google.com/automl/ .. _Client Library Documentation: https://cloud.google.com/python/docs/reference/automl/latest/summary_overview .. _Product Documentation: https://cloud.google.com/automl/

Quick Start

In order to use this library, you first need to go through the following steps:

Select or create a Cloud Platform project._
Enable billing for your project._
Enable the Cloud AutoML._
`Set up...

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AI interpretation — verify before relying

AI-generated interpretation of the package facts above; every digit, version, license, or vulnerability id it cites is grounded in the facts already shown on this page

Official Python client library for Google Cloud AutoML, enabling programmatic training and deployment of custom machine learning models through Google Cloud's managed service.

Low friction installation with stable maintenance: released 71 days ago, actively maintained repository with 5370 stars, and all dependencies are standard Google Cloud client libraries (google-api-core, google-auth, grpcio, proto-plus, protobuf).

Apache-2.0 permissive license allows commercial and private use with minimal restrictions; suitable for most projects requiring attribution.

Usage

pip install google-cloud-automl

from google.cloud import automl

client = automl.AutoMlClient()
project_id = 'your-project-id'
location = 'us-central1'
parent = client.common_location_path(project_id, location)

Requires Python >= 3.10; Google Cloud project with AutoML enabled and authentication credentials configured (see GOOGLE_APPLICATION_CREDENTIALS environment variable).

Verdict: Production-ready Google Cloud client library with active maintenance, no known vulnerabilities, and permissive licensing. Suitable for teams building ML workflows on Google Cloud, though it requires prior GCP project setup and authentication configuration.

Needs verification

Whether the package's five runtime dependencies (google-api-core, google-auth, grpcio, proto-plus, protobuf) introduce any transitive security concerns or version conflicts in typical deployment environments.
Performance characteristics and latency expectations when training or deploying models through this client in production workloads.

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