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azureml-pipeline

Used to build, optimize, and manage their machine learning workflows.

With conditionsPyPI Artificial IntelligenceReleased Feb 2026217.2K downloads / moPure Python

Decision gist · record as of 2026-08-14

pure-Python wheel — azureml_pipeline-1.62.0-py3-none-any.whl
v1.62.0 · released 2026-02-25 · Python <4.0,>=3.8 · 2 runtime deps: azureml-pipeline-core, azureml-pipeline-steps

Yes, if you are building ML workflows in Azure. The package is production-stable, actively maintained, has low install friction, and addresses a core use case for Azure ML users. The proprietary license requires verification of terms, but poses no blocker for typical development use within Azure. No known security vulnerabilities.AI-flagged interpretation of the facts on this page — verify before relying

Before you install

  • Requires an Azure Machine Learning workspace and authentication credentials to submit and track pipeline runs.
  • Low install friction with a pure-Python wheel and only two runtime dependencies.
  • Actively maintained as of 170 days ago with production-stable status.

License · maintenance · safety

(unclear) — Licensed under a proprietary Microsoft license (https://aka.ms/azureml-sdk-license) rather than an open-source SPDX license, which may restrict commercial or derivative use depending on the terms.

last release 2026-02-25 (170 days)

0 known vulnerabilities (OSV.dev, 2026-08-14) · 217,198 downloads/mo, #9,363 on PyPI

Verify before relying

pip install azureml-pipeline

from azureml.pipeline.core import Pipeline
from azureml.pipeline.steps import PythonScriptStep

# Create pipeline steps and compose into a Pipeline object
pipeline = Pipeline(workspace=ws, steps=[step1, step2])
  • Exact scope of the proprietary license terms and whether it permits commercial use or redistribution.
  • Whether azureml-pipeline-core and azureml-pipeline-steps are independently versioned or locked to this release.
Same gist for agents: .md · .json

What it is and what it does

azureml-pipeline is a Python SDK component for defining and managing machine learning workflows within Azure Machine Learning. It allows data scientists to compose reusable steps—such as data preparation, model training, hyperparameter tuning, and deployment—into executable pipelines that can be submitted to Azure for distributed execution and tracked over time.

The package depends on azureml-pipeline-core and azureml-pipeline-steps to provide the underlying pipeline infrastructure and step types. It supports Python 3.8 through 3.11 on Windows, macOS, and Linux, and is classified as production-stable. The package is part of the broader Azure ML SDK ecosystem and is intended for developers and researchers building ML workflows in Azure.

Use it for

  • Orchestrate multi-step data preparation and model training workflows that run on Azure compute resources.
  • Define reusable pipeline steps and modules to standardize ML processes across teams.
  • Submit and monitor long-running ML jobs without managing infrastructure directly.
  • Automate hyperparameter tuning and model validation as part of a repeatable pipeline.
  • Build end-to-end ML workflows from raw data ingestion through model deployment.

Worth the install?

AI-flagged interpretation of the facts on this page. Verify before relying on it.

With conditions

Yes, if you are building ML workflows in Azure.

The package is production-stable, actively maintained, has low install friction, and addresses a core use case for Azure ML users. The proprietary license requires verification of terms, but poses no blocker for typical development use within Azure. No known security vulnerabilities.

Install

azureml-pipeline on PyPI

Before you install

Low install friction with a pure-Python wheel and only two runtime dependencies. Actively maintained as of 170 days ago with production-stable status.

Requires an Azure Machine Learning workspace and authentication credentials to submit and track pipeline runs.

License in practice

Licensed under a proprietary Microsoft license (https://aka.ms/azureml-sdk-license) rather than an open-source SPDX license, which may restrict commercial or derivative use depending on the terms.

Quickstart

pip install azureml-pipeline

from azureml.pipeline.core import Pipeline
from azureml.pipeline.steps import PythonScriptStep

# Create pipeline steps and compose into a Pipeline object
pipeline = Pipeline(workspace=ws, steps=[step1, step2])

Verify before relying

  • Exact scope of the proprietary license terms and whether it permits commercial use or redistribution.
  • Whether azureml-pipeline-core and azureml-pipeline-steps are independently versioned or locked to this release.

Package facts

LicenseNot declared unclear
Python supportSupports the current Python release <4.0,>=3.8
Install frictionLow. Pure-Python wheel
Runtime dependencies
2 packages
azureml-pipeline-coreazureml-pipeline-steps
MaintenanceActively maintained 170 days since the last release
First released
Downloads217,198 / month, #9,363 on PyPI 30-day window, as of 2026-08-14
Known vulnerabilitiesNone known OSV.dev, checked 2026-08-14
Classifiers
Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: Other/Proprietary LicenseOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: POSIX :: LinuxProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Scientific/Engineering :: Artificial Intelligence

Evidence: azureml_pipeline-1.62.0-py3-none-any.whl

Tags

Capabilities
azure machine learning pipelinesml workflow orchestrationdata preparation and model trainingazure ml pipeline stepsmachine learning pipeline management
Topics
azure-mlworkflow-orchestrationml-pipelines

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See also azureml-pipeline-core · azureml-pipeline-steps · chalkpy · weasel · azureml-dataprep · azure-ml-component · azureml-dataprep-native · azureml-dataprep-rslex · azureml-sdk · model-index